RESEARCH CENTRE

Inria Centre
at the University of Bordeaux

2023
ACTIVITY REPORT

IN PARTNERSHIP WITH:

Université de Bordeaux, CNRS

Project-Team

MANAO

Melting the frontiers between Light, Shape
and Matter

IN COLLABORATION WITH: Laboratoire Bordelais de Recherche en
Informatique (LaBRI)

DOMAIN

Perception, Cognition and Interaction

THEME

Interaction and visualization

Contents

Project-Team MANAO

1 Team members, visitors, external collaborators

2 Overall objectives

2.1 General Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.1 Using a global approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2.2 Taking observers into account

1

2

3
3
4
4
4

3 Research program

5
5
3.1 Related Scientific Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
3.2 Research axes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6
3.3 Axis 1: Analysis and Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
3.4 Axis 2: From Acquisition to Display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.5 Axis 3: Rendering, Visualization and Illustration . . . . . . . . . . . . . . . . . . . . . . . . . .
9
3.6 Axis 4: Editing and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

4 Application domains

12
4.1 Physical Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
4.2 Interactive Visualization and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

5 Social and environmental responsibility

12
5.1 Footprint of research activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
5.2 Environmental involvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

6 Highlights of the year

13

7 New software, platforms, open data

13
7.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.1.1 Spectral Viewer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.1.2 Malia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.1.3 FRITE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
7.1.4 ecodiag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
7.1.5 FtthPowerSim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
7.2 New platforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
7.2.1 La Coupole . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
7.2.2 Measuring Bidirectional Subscattering Reflectance and Transmission Functions . . 15

8 New results

16
8.1 Analysis and Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
8.1.1 Material category of visual objects computed from specular image structure . . . . . 16
8.2 From Acquisition to Display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
8.2.1 Depth from Focus using Windowed Linear Least Squares Regressions . . . . . . . . . 17
8.3 Rendering, Visualization and Illustration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
8.3.1 A Micrograin BSDF Model for the Rendering of Porous Layers . . . . . . . . . . . . . . 18
8.4 Editing and Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
8.4.1 Non-linear Rough 2D Animation using Transient Embeddings . . . . . . . . . . . . . 19
8.4.2 Efficient Interpolation of Rough Line Drawings . . . . . . . . . . . . . . . . . . . . . . 19
8.4.3 One-to-Many Spectral Upsampling of Reflectances and Transmittances
. . . . . . . 20
8.4.4 FlavoMetrics: Towards a Digital Tool to Understand and Tune Living Aesthetics of

Flavobacteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

8.4.5 ImmersiveIML - Immersive interactive machine learning for 3D point cloud classific-

ation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
8.5 Modeling environnemental impacts of digitization . . . . . . . . . . . . . . . . . . . . . . . . 23

9 Bilateral contracts and grants with industry

23
9.1 Bilateral contracts with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

10 Partnerships and cooperations

10.1.1 Visits to international teams

24
10.1 International initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
10.2 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
10.2.1 ANR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
10.2.2 Inria Exploratory actions
10.3 Regional initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

11 Dissemination

25
11.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
11.1.1 Journal
11.1.2 Invited talks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
11.1.3 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
11.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
11.3 Popularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

12 Scientific production

27
12.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
12.2 Publications of the year
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
12.3 Other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
12.4 Cited publications

Project MANAO

1

Project-Team MANAO

Creation of the Project-Team: 2014 July 01

Keywords

Computer sciences and digital sciences

A5. – Interaction, multimedia and robotics

A5.1.1. – Engineering of interactive systems

A5.1.6. – Tangible interfaces

A5.3.5. – Computational photography

A5.4. – Computer vision

A5.4.4. – 3D and spatio-temporal reconstruction

A5.5. – Computer graphics

A5.5.1. – Geometrical modeling

A5.5.2. – Rendering

A5.5.3. – Computational photography

A5.5.4. – Animation

A5.6. – Virtual reality, augmented reality

A6.2.3. – Probabilistic methods

A6.2.5. – Numerical Linear Algebra

A6.2.6. – Optimization

A6.2.8. – Computational geometry and meshes

Other research topics and application domains

B3.1. – Sustainable development

B3.1.1. – Resource management

B3.6. – Ecology

B5. – Industry of the future

B5.1. – Factory of the future

B9. – Society and Knowledge

B9.2. – Art

B9.2.2. – Cinema, Television

B9.2.3. – Video games

B9.6. – Humanities

B9.6.1. – Psychology

B9.6.6. – Archeology, History

B9.6.10. – Digital humanities

2

Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Pascal Barla [Team leader, INRIA, Researcher, HDR]

• Gael Guennebaud [INRIA, Researcher]

• Romain Pacanowski [INRIA, Researcher, HDR]

Faculty Members

• Pierre Benard [UNIV BORDEAUX, Associate Professor]

• Patrick Reuter [UNIV BORDEAUX, Associate Professor, HDR]

Post-Doctoral Fellows

• Jean Basset [INRIA, Post-Doctoral Fellow, from Feb 2023]

• Morgane Gerardin [INRIA, Post-Doctoral Fellow, until Jun 2023]

• Pierre Mezieres [INRIA, Post-Doctoral Fellow, from Jul 2023]

PhD Students

• Corentin Cou [CNRS]

• Melvin Even [INRIA]

• Gary Fourneau [INRIA]

• Pierre La Rocca [UNIV BORDEAUX]

• Simon Lucas [UNIV BORDEAUX]

• Lea Marquet [INRIA, from Nov 2023]

• Panagiotis Tsiapkolis [UBISOFT, CIFRE]

Technical Staff

• Marjorie Paillet [INRIA, Engineer, until Oct 2023]

Interns and Apprentices

• Pablo Leboulanger [UNIV BORDEAUX, Intern, from Mar 2023 until Jul 2023]

• Bastien Morel [INRIA, Intern, from Mar 2023 until Aug 2023]

• Coralie Rigaut [INRIA, Intern, from Feb 2023 until Jul 2023]

Administrative Assistant

• Anne-Laure Gautier [INRIA]

External Collaborators

• Aurelie Bugeau [UNIV BORDEAUX, from Sep 2023, HDR]

• Morgane Gerardin [UNIV TOULOUSE III, from Jul 2023]

• Xavier Granier [UNIV PARIS SACLAY, HDR]

Project MANAO

3

2 Overall objectives

2.1 General Introduction

Computer generated images are ubiquitous in our everyday life. Such images are the result of a process
that has seldom changed over the years: the optical phenomena due to the propagation of light in a 3D
environment are simulated taking into account how light is scattered [61, 38] according to shape and
material characteristics of objects. The intersection of optics (for the underlying laws of physics) and
computer science (for its modeling and computational efficiency aspects) provides a unique opportunity
to tighten the links between these domains in order to first improve the image generation process (com-
puter graphics, optics and virtual reality) and next to develop new acquisition and display technologies
(optics, mixed reality and machine vision).

Most of the time, light, shape, and matter properties are studied, acquired, and modeled separately,
relying on realistic or stylized rendering processes to combine them in order to create final pixel colors.
Such modularity, inherited from classical physics, has the practical advantage of permitting to reuse the
same models in various contexts. However, independent developments lead to un-optimized pipelines
and difficult-to-control solutions since it is often not clear which part of the expected result is caused
by which property. Indeed, the most efficient solutions are most often the ones that blur the frontiers
between light, shape, and matter to lead to specialized and optimized pipelines, as in real-time applic-
ations (like Bidirectional Texture Functions [71] and Light-Field rendering [36]). Keeping these three
properties separated may lead to other problems. For instance:

• Measured materials are too detailed to be usable in rendering systems and data reduction tech-
niques have to be developed [70, 72], leading to an inefficient transfer between real and digital
worlds;

• It is currently extremely challenging (if not impossible) to directly control or manipulate the
interactions between light, shape, and matter. Accurate lighting processes may create solutions
that do not fulfill users’ expectations;

• Artists can spend hours and days in modeling highly complex surfaces whose details will not be

visible [92] due to inappropriate use of certain light sources or reflection properties.

Most traditional applications target human observers. Depending on how deep we take into account

the specificity of each user, the requirement of representations, and algorithms may differ.

Auto-stereoscopy display
©Nintendo

HDR display
©Dolby Digital

Printing both geometry and material
[53]

Figure 1: Examples of new display technologies. Nowadays, they are not limited to a simple array of 2D
low-dynamic RGB values.

With the evolution of measurement and display technologies that go beyond conventional images
(e.g., as illustrated in Figure 1, High-Dynamic Range Imaging [82], stereo displays or new display tech-
nologies [57], and physical fabrication [28, 45, 53]) the frontiers between real and virtual worlds are
vanishing [41]. In this context, a sensor combined with computational capabilities may also be con-
sidered as another kind of observer. Creating separate models for light, shape, and matter for such an
extended range of applications and observers is often inefficient and sometimes provides unexpected
results. Pertinent solutions must be able to take into account properties of the observer (human or
machine) and application goals.

4

Inria Annual Report 2023

2.2 Methodology

Figure 2: Interactions/Transfers between real and virtual worlds. One of our goal is to combine optical
instruments with processes from computer science in order to blend the two worlds.

2.2.1 Using a global approach

The main goal of the MANAO project is to study phenomena resulting from the interactions between the
three components that describe light propagation and scattering in a 3D environment: light, shape, and
matter. Improving knowledge about these phenomena facilitates the adaption of the developed digital,
numerical, and analytic models to specific contexts. This leads to the development of new analysis tools,
new representations, and new instruments for acquisition, visualization, and display.

To reach this goal, we have to first increase our understanding of the different phenomena resulting
from the interactions between light, shape, and matter. For this purpose, we consider how they are
captured or perceived by the final observer, taking into account the relative influence of each of the three
components. Examples include but are not limited to:

• The manipulation of light to reveal reflective [33] or geometric properties [99], as mastered by

professional photographers;

• The modification of material characteristics or lighting conditions [98] to better understand shape

features, for instance to decipher archaeological artifacts;

• The large influence of shape on the captured variation of shading [80] and thus on the perception

of material properties [95].

Based on the acquired knowledge of the influence of each of the components, we aim at developing new
models that combine two or three of these components. Examples include the modeling of Bidirectional
Texture Functions (BTFs) [44] that encode in a unique representation effects of parallax, multiple light
reflections, and also shadows without requiring to store separately the reflective properties and the
meso-scale geometric details, or Light-Fields that are used to render 3D scenes by storing only the result
of the interactions between light, shape, and matter both in complex real environments and in simulated
ones.

One of the strengths of MANAO is that we are inter-connecting computer graphics and optics (Fig-
ure 2). On one side, the laws of physics are required to create images but may be bent to either increase
performance or user’s control: this is one of the key advantage of computer graphics approach. It is worth
noticing that what is not possible in the real world may be possible in a digital world. However, on the
other side, the introduced approximations may help to better comprehend the physical interactions of
light, shape, and matter.

2.2.2 Taking observers into account

The MANAO project specifically aims at considering information transfer, first from the real world
to the virtual world (acquisition and creation), then from computers to observers (visualization and
display). For this purpose, we use a larger definition of what an observer is: it may be a human user or a
physical sensor equipped with processing capabilities. Sensors and their characteristics must be taken
into account in the same way as we take into account the human visual system in computer graphics.
Similarly, computational capabilities may be compared to cognitive capabilities of human users. Some

Project MANAO

5

characteristics are common to all observers, such as the scale of observed phenomena. Some others are
more specifics to a set of observers. For this purpose, we have identified two classes of applications.

• Physical systems Provided our partnership that leads to close relationships with optics, one novelty
of our approach is to extend the range of possible observers to physical sensors in order to work
on domains such as simulation, mixed reality, and testing. Capturing, processing, and visualizing
complex data is now more and more accessible to everyone, leading to the possible convergence
of real and virtual worlds through visual signals. This signal is traditionally captured by cameras.
It is now possible to augment them by projecting (e.g., the infrared laser of Microsoft Kinect) and
capturing (e.g., GPS localization) other signals that are outside the visible range. These supple-
mental information replace values traditionally extracted from standard images and thus lower
down requirements in computational power [68]. Since the captured images are the result of the
interactions between light, shape, and matter, the approaches and the improved knowledge from
MANAO help in designing interactive acquisition and rendering technologies that are required
to merge the real and the virtual world. With the resulting unified systems (optical and digital),
transfer of pertinent information is favored and inefficient conversion is likely avoided, leading
to new uses in interactive computer graphics applications, like augmented reality [32, 41] and
computational photography [81].

• Interactive visualization This direction includes domains such as scientific illustration and visu-
alization, artistic or plausible rendering. In all these cases, the observer, a human, takes part in
the process, justifying once more our focus on real-time methods. When targeting average users,
characteristics as well as limitations of the human visual system should be taken into account:
in particular, it is known that some configurations of light, shape, and matter have masking and
facilitation effects on visual perception [92]. For specialized applications, the expertise of the final
user and the constraints for 3D user interfaces lead to new uses and dedicated solutions for models
and algorithms.

3 Research program

3.1 Related Scientific Domains

Figure 3: Related scientific domains of the MANAO project.

The MANAO project aims at studying, acquiring, modeling, and rendering the interactions between
the three components that are light, shape, and matter from the viewpoint of an observer. As detailed
more lengthily in the next section, such a work will be done using the following approach: first, we will
tend to consider that these three components do not have strict frontiers when considering their impacts
on the final observers; then, we will not only work in computer graphics, but also at the intersection of
computer graphics and optics, exploring the mutual benefits that the two domains may provide. It is thus
intrinsically a transdisciplinary project (as illustrated in Figure 3) and we expect results in both domains.
Thus, the proposed team-project aims at establishing a close collaboration between computer graph-
ics (e.g., 3D modeling, geometry processing, shading techniques, vector graphics, and GPU programming)
and optics (e.g., design of optical instruments, and theories of light propagation). The following examples
illustrate the strengths of such a partnership. First, in addition to simpler radiative transfer equations [46]
commonly used in computer graphics, research in the later will be based on state-of-the-art under-
standing of light propagation and scattering in real environments. Furthermore, research will rely on
appropriate instrumentation expertise for the measurement [58, 59] and display [57] of the different

6

Inria Annual Report 2023

phenomena. Reciprocally, optics researches may benefit from the expertise of computer graphics scient-
ists on efficient processing to investigate interactive simulation, visualization, and design. Furthermore,
new systems may be developed by unifying optical and digital processing capabilities. Currently, the
scientific background of most of the team members is related to computer graphics and computer vision.
A large part of their work have been focused on simulating and analyzing optical phenomena as well
as in acquiring and visualizing them. Combined with the close collaboration with the optics laboratory
LP2N and with the students issued from the “Institut d’Optique”, this background ensures that we can
expect the following results from the project: the construction of a common vocabulary for tightening
the collaboration between the two scientific domains and creating new research topics. By creating this
context, we expect to attract (and even train) more trans-disciplinary researchers.

At the boundaries of the MANAO project lie issues in human and machine vision. We have to deal
with the former whenever a human observer is taken into account. On one side, computational models of
human vision are likely to guide the design of our algorithms. On the other side, the study of interactions
between light, shape, and matter may shed some light on the understanding of visual perception. The
same kind of connections are expected with machine vision. On the one hand, traditional computational
methods for acquisition (such as photogrammetry) are going to be part of our toolbox. On the other
hand, new display technologies (such as the ones used for augmented reality) are likely to benefit from
our integrated approach and systems. In the MANAO project we are mostly users of results from human
vision. When required, some experimentation might be done in collaboration with experts from this
domain, like with the European PRISM project. For machine vision, provided the tight collaboration
between optical and digital systems, research will be carried out inside the MANAO project.

Analysis and modeling rely on tools from applied mathematics such as differential and projective
geometry, multi-scale models, frequency analysis [48] or differential analysis [80], linear and non-linear
approximation techniques, stochastic and deterministic integrations, and linear algebra. We not only rely
on classical tools, but also investigate and adapt recent techniques (e.g., improvements in approximation
techniques), focusing on their ability to run on modern hardware: the development of our own tools
(such as Eigen) is essential to control their performances and their abilities to be integrated into real-time
solutions or into new instruments.

3.2 Research axes

The MANAO project is organized around four research axes that cover the large range of expertise of its
members and associated members. We briefly introduce these four axes in this section. More details and
their inter-influences that are illustrated in the Figure 2 will be given in the following sections.

Axis 1 is the theoretical foundation of the project. Its main goal is to increase the understanding
of light, shape, and matter interactions by combining expertise from different domains: optics and
human/machine vision for the analysis and computer graphics for the simulation aspect. The goal of our
analyses is to identify the different layers/phenomena that compose the observed signal. In a second
step, the development of physical simulations and numerical models of these identified phenomena is a
way to validate the pertinence of the proposed decompositions.

In Axis 2, the final observers are mainly physical captors. Our goal is thus the development of new
acquisition and display technologies that combine optical and digital processes in order to reach fast
transfers between real and digital worlds, in order to increase the convergence of these two worlds.

Axes 3 and 4 focus on two aspects of computer graphics: rendering, visualization and illustration in
Axis 3, and editing and modeling (content creation) in Axis 4. In these two axes, the final observers are
mainly human users, either generic users or expert ones (e.g., archaeologist [84], computer graphics
artists).

3.3 Axis 1: Analysis and Simulation

Challenge: Definition and understanding of phenomena resulting from interactions between light, shape,
and matter as seen from an observer point of view.

Results: Theoretical tools and numerical models for analyzing and simulating the observed optical

phenomena.

Project MANAO

7

To reach the goals of the MANAO project, we need to increase our understanding of how light, shape,
and matter act together in synergy and how the resulting signal is finally observed. For this purpose,
we need to identify the different phenomena that may be captured by the targeted observers. This is
the main objective of this research axis, and it is achieved by using three approaches: the simulation
of interactions between light, shape, and matter, their analysis and the development of new numerical
models. This resulting improved knowledge is a foundation for the researches done in the three other
axes, and the simulation tools together with the numerical models serve the development of the joint
optical/digital systems in Axis 2 and their validation.

One of the main and earliest goals in computer graphics is to faithfully reproduce the real world,
focusing mainly on light transport. Compared to researchers in physics, researchers in computer graphics
rely on a subset of physical laws (mostly radiative transfer and geometric optics), and their main concern
is to efficiently use the limited available computational resources while developing as fast as possible
algorithms. For this purpose, a large set of theoretical as well as computational tools has been introduced
to take a maximum benefit of hardware specificities. These tools are often dedicated to specific phenom-
ena (e.g., direct or indirect lighting, color bleeding, shadows, caustics). An efficiency-driven approach
needs such a classification of light paths [54] in order to develop tailored strategies [96]. For instance,
starting from simple direct lighting, more complex phenomena have been progressively introduced:
first diffuse indirect illumination [52, 88], then more generic inter-reflections [61, 46] and volumetric
scattering [85, 43]. Thanks to this search for efficiency and this classification, researchers in computer
graphics have developed a now recognized expertise in fast-simulation of light propagation. Based on
finite elements (radiosity techniques) or on unbiased Monte Carlo integration schemes (ray-tracing,
particle-tracing, ...), the resulting algorithms and their combination are now sufficiently accurate to
be used-back in physical simulations. The MANAO project will continue the search for efficient and
accurate simulation techniques, but extending it from computer graphics to optics. Thanks to the close
collaboration with scientific researchers from optics, new phenomena beyond radiative transfer and
geometric optics will be explored.

Search for algorithmic efficiency and accuracy has to be done in parallel with numerical models. The
goal of visual fidelity (generalized to accuracy from an observer point of view in the project) combined
with the goal of efficiency leads to the development of alternative representations. For instance, common
classical finite-element techniques compute only basis coefficients for each discretization element: the
required discretization density would be too large and to computationally expensive to obtain detailed
spatial variations and thus visual fidelity. Examples includes texture for decorrelating surface details from
surface geometry and high-order wavelets for a multi-scale representation of lighting [42]. The numerical
complexity explodes when considering directional properties of light transport such as radiance intensity
−1), reducing the possibility to simulate or accurately
(Watt per square meter and per steradian - W.m
represent some optical phenomena. For instance, Haar wavelets have been extended to the spherical
domain [87] but are difficult to extend to non-piecewise-constant data [90]. More recently, researches
prefer the use of Spherical Radial Basis Functions [93] or Spherical Harmonics [79]. For more complex
data, such as reflective properties (e.g., BRDF [73, 62] - 4D), ray-space (e.g., Light-Field [69] - 4D), spatially
varying reflective properties (6D - [83]), new models, and representations are still investigated such as
rational functions [76] or dedicated models [30] and parameterizations [86, 91]. For each (newly) defined
phenomena, we thus explore the space of possible numerical representations to determine the most
suited one for a given application, like we have done for BRDF [76].

−2.sr

Before being able to simulate or to represent the different observed phenomena, we need to define
and describe them. To understand the difference between an observed phenomenon and the classical
light, shape, and matter decomposition, we can take the example of a highlight. Its observed shape (by
a human user or a sensor) is the resulting process of the interaction of these three components, and
can be simulated this way. However, this does not provide any intuitive understanding of their relative
influence on the final shape: an artist will directly describe the resulting shape, and not each of the three
properties. We thus want to decompose the observed signal into models for each scale that can be easily
understandable, representable, and manipulable. For this purpose, we will rely on the analysis of the
resulting interaction of light, shape, and matter as observed by a human or a physical sensor. We first
consider this analysis from an optical point of view, trying to identify the different phenomena and their
scale according to their mathematical properties (e.g., differential [80] and frequency analysis [48]). Such
an approach has leaded us to exhibit the influence of surfaces flows (depth and normal gradients) into

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Inria Annual Report 2023

Texuring

1st order gradient field

Environment reflection

2st order gradient field

Figure 4: First-order analysis [97] have shown that shading variations are caused by depth variations
(first-order gradient field) and by normal variations (second-order fields). These fields are visualized
using hue and saturation to indicate direction and magnitude of the flow respectively.

lighting pattern deformation (see Figure 4). For a human observer, this correspond to one recent trend
in computer graphics that takes into account the human visual systems [49] both to evaluate the results
and to guide the simulations.

3.4 Axis 2: From Acquisition to Display

Challenge: Convergence of optical and digital systems to blend real and virtual worlds.

Results: Instruments to acquire real world, to display virtual world, and to make both of them interact.

Figure 5: Light-Field transfer: global illumination between real and synthetic objects [41]

In this axis, we investigate unified acquisition and display systems, that is systems which combine op-
tical instruments with digital processing. From digital to real, we investigate new display approaches [69,
57]. We consider projecting systems and surfaces [37], for personal use, virtual reality and augmented
reality [32]. From the real world to the digital world, we favor direct measurements of parameters for
models and representations, using (new) optical systems unless digitization is required [51], [50]. These
resulting systems have to acquire the different phenomena described in Axis 1 and to display them, in an
efficient manner [55, 31, 56, 59]. By efficient, we mean that we want to shorten the path between the real
world and the virtual world by increasing the data bandwidth between the real (analog) and the virtual
(digital) worlds, and by reducing the latency for real-time interactions (we have to prevent unnecessary
conversions, and to reduce processing time). To reach this goal, the systems have to be designed as a
whole, not by a simple concatenation of optical systems and digital processes, nor by considering each
component independently [60].

To increase data bandwidth, one solution is to parallelize more and more the physical systems. One
possible solution is to multiply the number of simultaneous acquisitions (e.g., simultaneous images
from multiple viewpoints [59, 78]). Similarly, increasing the number of viewpoints is a way toward the
creation of full 3D displays [69]. However, full acquisition or display of 3D real environments theoretically
requires a continuous field of viewpoints, leading to huge data size. Despite the current belief that the
increase of computational power will fill the missing gap, when it comes to visual or physical realism, if
you double the processing power, people may want four times more accuracy, thus increasing data size
as well. To reach the best performances, a trade-off has to be found between the amount of data required

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to represent accurately the reality and the amount of required processing. This trade-off may be achieved
using compressive sensing. Compressive sensing is a new trend issued from the applied mathematics
community that provides tools to accurately reconstruct a signal from a small set of measurements
assuming that it is sparse in a transform domain (e.g., [77, 102]).

We prefer to achieve this goal by avoiding as much as possible the classical approach where acquisition
is followed by a fitting step: this requires in general a large amount of measurements and the fitting itself
may consume consequently too much memory and preprocessing time. By preventing unnecessary
conversion through fitting techniques, such an approach increase the speed and reduce the data transfer
for acquisition but also for display. One of the best recent examples is the work of Cossairt et al. [41]. The
whole system is designed around a unique representation of the energy-field issued from (or leaving) a
3D object, either virtual or real: the Light-Field. A Light-Field encodes the light emitted in any direction
from any position on an object. It is acquired thanks to a lens-array that leads to the capture of, and
projection from, multiple simultaneous viewpoints. A unique representation is used for all the steps of
this system. Lens-arrays, parallax barriers, and coded-aperture [67] are one of the key technologies to
develop such acquisition (e.g., Light-Field camera 1 [60] and acquisition of light-sources [51]), projection
systems (e.g., auto-stereoscopic displays). Such an approach is versatile and may be applied to improve
classical optical instruments [65]. More generally, by designing unified optical and digital systems [74], it
is possible to leverage the requirement of processing power, the memory footprint, and the cost of optical
instruments.

Those are only some examples of what we investigate. We also consider the following approaches to
develop new unified systems. First, similar to (and based on) the analysis goal of Axis 1, we have to take
into account as much as possible the characteristics of the measurement setup. For instance, when fitting
cannot be avoided, integrating them may improve both the processing efficiency and accuracy [76].
Second, we have to integrate signals from multiple sensors (such as GPS, accelerometer, ...) to prevent
some computation (e.g., [68]). Finally, the experience of the group in surface modeling help the design of
optical surfaces [63] for light sources or head-mounted displays.

3.5 Axis 3: Rendering, Visualization and Illustration

Challenge: How to offer the most legible signal to the final observer in real-time?

Results: High-level shading primitives, expressive rendering techniques for object depiction, real-time

realistic rendering algorithms

Realistic

Rendering

Visualization

and Illustration

(a) Global illumination [75]

(b) Shadows [103]

(c) Shape enhancement [98]

(d) Shape depiction [29]

Figure 6: In the MANAO project, we are investigating rendering techniques from realistic solutions (e.g.,
inter-reflections (a) and shadows (b)) to more expressive ones (shape enhancement (c) with realistic style
and shape depiction (d) with stylized style) for visualization.

The main goal of this axis is to offer to the final observer, in this case mostly a human user, the most
legible signal in real-time. Thanks to the analysis and to the decomposition in different phenomena
resulting from interactions between light, shape, and matter (Axis 1), and their perception, we can use
them to convey essential information in the most pertinent way. Here, the word pertinent can take various
forms depending on the application.

In the context of scientific illustration and visualization, we are primarily interested in tools to convey
shape or material characteristics of objects in animated 3D scenes. Expressive rendering techniques

1Lytro

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Inria Annual Report 2023

(see Figure 6c,d) provide means for users to depict such features with their own style. To introduce
our approach, we detail it from a shape-depiction point of view, domain where we have acquired a
recognized expertise. Prior work in this area mostly focused on stylization primitives to achieve line-
based rendering [100], [64] or stylized shading [35], [98] with various levels of abstraction. A clear
representation of important 3D object features remains a major challenge for better shape depiction,
stylization and abstraction purposes. Most existing representations provide only local properties (e.g.,
curvature), and thus lack characterization of broader shape features. To overcome this limitation, we
are developing higher level descriptions of shape [29] with increased robustness to sparsity, noise, and
outliers. This is achieved in close collaboration with Axis 1 by the use of higher-order local fitting methods,
multi-scale analysis, and global regularization techniques. In order not to neglect the observer and the
material characteristics of the objects, we couple this approach with an analysis of the appearance model.
To our knowledge, this is an approach which has not been considered yet. This research direction is
at the heart of the MANAO project, and has a strong connection with the analysis we plan to conduct
in Axis 1. Material characteristics are always considered at the light ray level, but an understanding of
higher-level primitives (like the shape of highlights and their motion) would help us to produce more
legible renderings and permit novel stylizations; for instance, there is no method that is today able to
create stylized renderings that follow the motion of highlights or shadows. We also believe such tools
also play a fundamental role for geometry processing purposes (such as shape matching, reassembly,
simplification), as well as for editing purposes as discussed in Axis 4.

In the context of real-time photo-realistic rendering ((see Figure 6a,b), the challenge is to compute
the most plausible images with minimal effort. During the last decade, a lot of work has been devoted
to design approximate but real-time rendering algorithms of complex lighting phenomena such as
soft-shadows [101], motion blur [48], depth of field [89], reflexions, refractions, and inter-reflexions.
For most of these effects it becomes harder to discover fundamentally new and faster methods. On
the other hand, we believe that significant speedup can still be achieved through more clever use of
massively parallel architectures of the current and upcoming hardware, and/or through more clever
tuning of the current algorithms. In particular, regarding the second aspect, we remark that most of
the proposed algorithms depend on several parameters which can be used to trade the speed over the
quality. Significant speed-up could thus be achieved by identifying effects that would be masked or
facilitated and thus devote appropriate computational resources to the rendering [66], [47]. Indeed, the
algorithm parameters controlling the quality vs speed are numerous without a direct mapping between
their values and their effect. Moreover, their ideal values vary over space and time, and to be effective
such an auto-tuning mechanism has to be extremely fast such that its cost is largely compensated by
its gain. We believe that our various work on the analysis of the appearance such as in Axis 1 could be
beneficial for such purpose too.

Realistic and real-time rendering is closely related to Axis 2: real-time rendering is a requirement to
close the loop between real world and digital world. We have to thus develop algorithms and rendering
primitives that allow the integration of the acquired data into real-time techniques. We have also to take
care of that these real-time techniques have to work with new display systems. For instance, stereo, and
more generally multi-view displays are based on the multiplication of simultaneous images. Brute force
solutions consist in independent rendering pipeline for each viewpoint. A more energy-efficient solution
would take advantages of the computation parts that may be factorized. Another example is the rendering
techniques based on image processing, such as our work on augmented reality [39]. Independent image
processing for each viewpoint may disturb the feeling of depth by introducing inconsistent information
in each images. Finally, more dedicated displays [57] would require new rendering pipelines.

3.6 Axis 4: Editing and Modeling

Challenge: Editing and modeling appearance using drawing- or sculpting-like tools through high level
representations.

Results: High-level primitives and hybrid representations for appearance and shape.
During the last decade, the domain of computer graphics has exhibited tremendous improvements
in image quality, both for 2D applications and 3D engines. This is mainly due to the availability of
an ever increasing amount of shape details, and sophisticated appearance effects including complex
lighting environments. Unfortunately, with such a growth in visual richness, even so-called vectorial

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representations (e.g., subdivision surfaces, Bézier curves, gradient meshes, etc.) become very dense and
unmanageable for the end user who has to deal with a huge mass of control points, color labels, and
other parameters. This is becoming a major challenge, with a necessity for novel representations. This
Axis is thus complementary of Axis 3: the focus is the development of primitives that are easy to use for
modeling and editing.

More specifically, we plan to investigate vectorial representations that would be amenable to the
production of rich shapes with a minimal set of primitives and/or parameters. To this end we plan to
build upon our insights on dynamic local reconstruction techniques and implicit surfaces [40] [34].
When working in 3D, an interesting approach to produce detailed shapes is by means of procedural
geometry generation. For instance, many natural phenomena like waves or clouds may be modeled using
a combination of procedural functions. Turning such functions into triangle meshes (main rendering
primitives of GPUs) is a tedious process that appears not to be necessary with an adapted vectorial shape
representation where one could directly turn procedural functions into implicit geometric primitives.
Since we want to prevent unnecessary conversions in the whole pipeline (here, between modeling and
rendering steps), we will also consider hybrid representations mixing meshes and implicit representa-
tions. Such research has thus to be conducted while considering the associated editing tools as well as
performance issues. It is indeed important to keep real-time performance (cf. Axis 2) throughout the
interaction loop, from user inputs to display, via editing and rendering operations. Finally, it would be
interesting to add semantic information into 2D or 3D geometric representations. Semantic geometry
appears to be particularly useful for many applications such as the design of more efficient manipulation
and animation tools, for automatic simplification and abstraction, or even for automatic indexing and
searching. This constitutes a complementary but longer term research direction.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 7: Based on our analysis [97] (Axis 1), we have designed a system that mimics texture (left) and
shading (right) effects using image processing alone. It takes depth (a) and normal (d) images as input,
and uses them to deform images (b-e) in ways that closely approximate surface flows (c-f). It provides a
convincing, yet artistically controllable illusion of 3D shape conveyed through texture or shading cues.

In the MANAO project, we want to investigate representations beyond the classical light, shape, and
matter decomposition. We thus want to directly control the appearance of objects both in 2D and 3D
applications (e.g., [94]): this is a core topic of computer graphics. When working with 2D vector graphics,
digital artists must carefully set up color gradients and textures: examples range from the creation of
2D logos to the photo-realistic imitation of object materials. Classic vector primitives quickly become
impractical for creating illusions of complex materials and illuminations, and as a result an increasing
amount of time and skill is required. This is only for still images. For animations, vector graphics are only
used to create legible appearances composed of simple lines and color gradients. There is thus a need
for more complex primitives that are able to accommodate complex reflection or texture patterns, while
keeping the ease of use of vector graphics. For instance, instead of drawing color gradients directly, it is
more advantageous to draw flow lines that represent local surface concavities and convexities. Going
through such an intermediate structure then allows to deform simple material gradients and textures in
a coherent way (see Figure 7), and animate them all at once. The manipulation of 3D object materials
also raises important issues. Most existing material models are tailored to faithfully reproduce physical
behaviors, not to be easily controllable by artists. Therefore artists learn to tweak model parameters
to satisfy the needs of a particular shading appearance, which can quickly become cumbersome as
the complexity of a 3D scene increases. We believe that an alternative approach is required, whereby
material appearance of an object in a typical lighting environment is directly input (e.g., painted or
drawn), and adapted to match a plausible material behavior. This way, artists will be able to create

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their own appearance (e.g., by using our shading primitives [94]), and replicate it to novel illumination
environments and 3D models. For this purpose, we will rely on the decompositions and tools issued from
Axis 1.

4 Application domains

4.1 Physical Systems

Given our close relationships with researchers in optics, one novelty of our approach is to extend the
range of possible observers to physical sensors in order to work on domains such as simulation, mixed
reality, and testing. Capturing, processing, and visualizing complex data is now more and more accessible
to everyone, leading to the possible convergence of real and virtual worlds through visual signals. This
signal is traditionally captured by cameras. It is now possible to augment them by projecting (e.g., the
infrared laser of Microsoft Kinect) and capturing (e.g., GPS localization) other signals that are outside
the visible range. This supplemental information replaces values traditionally extracted from standard
images and thus lowers down requirements in computational power. Since the captured images are the
result of the interactions between light, shape, and matter, the approaches and the improved knowledge
from MANAO help in designing interactive acquisition and rendering technologies that are required to
merge the real and the virtual worlds. With the resulting unified systems (optical and digital), transfer
of pertinent information is favored and inefficient conversion is likely avoided, leading to new uses
in interactive computer graphics applications, like augmented reality, displays and computational
photography.

4.2 Interactive Visualization and Modeling

This direction includes domains such as scientific illustration and visualization, artistic or plausible
rendering, and 3D modeling. In all these cases, the observer, a human, takes part in the process, justifying
once more our focus on real-time methods. When targeting average users, characteristics as well as
limitations of the human visual system should be taken into account: in particular, it is known that some
configurations of light, shape, and matter have masking and facilitation effects on visual perception. For
specialized applications (such as archeology), the expertise of the final user and the constraints for 3D
user interfaces lead to new uses and dedicated solutions for models and algorithms.

5 Social and environmental responsibility

5.1 Footprint of research activities

For a few years now, our team has been collectively careful in limiting its direct environmental impacts,
mainly by limiting the number of flights and extending the lifetime of PCs and laptops beyond the
warranty period.

5.2 Environmental involvement

Gaël Guennebaud and Pascal Barla are engaged in several actions and initiatives related to the environ-
mental issues, both within Inria itself, and within the research and higher-education domain in general:

• They have been members of the “Local Sustainable Development Committee” of Inria Bordeaux

until November 2023.

• They are animators of the “The Climate Fresk”.

• G. Guennebaud is a member of the “Social and Environmental Responsibility Committee” of the

LaBRI and of the “transition network” of the University of Bordeaux.

• Ecoinfo: G. Guennebaud is involved within the GDS Ecoinfo of the CNRS, and in particular he is in

charge of the development of the ecodiag tool among other activities.

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• Labo1point5: G. Guennebaud is part of the labos1point5 GDR, in particular to help with the
development of a module to take into account the carbon footprint of ICT devices within their
carbon footprint estimation tool (GES1point5). The first version of the module has been released
in October 2021.

• G. Guennebaud participate in the elaboration and dissemination of an introductory course to
climate change issues and environnemental impacts of ICT, with two colleagues of the LaBRI.

• P. Barla and G. Guennebaud have compiled research projects conducted by Inria teams around
environmental issues within Bordeaux research center, and prepared material for presentation to
different audiences.

6 Highlights of the year

A publication in the presitigious “Natural Human Behaviour” journal [11], on the perception of material
categories from specular image features, in collaboration with researchers in Psychophysics at the
University of Giessen, Germany.

7 New software, platforms, open data

7.1 New software

7.1.1 Spectral Viewer

Keyword: Image

Functional Description: An open-source (spectral) image viewer that supports several images formats:

ENVI (spectral), exr, png, jpg.

URL: https://mrf-devteam.gitlab.io/spectral-viewer/

Authors: Arthur Dufay, Romain Pacanowski, Pascal Barla, Alban Fichet, David Murray

Contact: Romain Pacanowski

Partner: LP2N (CNRS - UMR 5298)

7.1.2 Malia

Name: The Malia Rendering Framework

Keywords: 3D, Realistic rendering, GPU, Ray-tracing, Material appearance

Functional Description: The Malia Rendering Framework is an open source library for predictive,
physically-realistic rendering. It comes with several applications, including a spectral path tracer,
RGB-to-spectral conversion routines a blender bridge and a spectral image viewer.

URL: https://pacanows.gitlabpages.inria.fr/MRF/main.md.html

Authors: Arthur Dufay, Romain Pacanowski, Pascal Barla, David Murray, Alban Fichet

Contact: Romain Pacanowski

7.1.3 FRITE

Keywords: 2D animation, Vector-based drawing

Functional Description: 2D animation software allowing non linear editing

Authors: Pierre Benard, Pascal Barla

Contact: Pierre Benard

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7.1.4 ecodiag

Inria Annual Report 2023

Keywords: CO2, Carbon footprint, Web Services

Functional Description: Ecodiag is a web service to estimate the carbon footprint of the computer

equipments.

Contact: Gael Guennebaud

Partner: Ecoinfo

7.1.5 FtthPowerSim

Keywords: Power consumption, Network simulator

Functional Description: This web application is a demo of the methodology and model detailed in the
paper Assessing VoD pressure on network power consumption. It’s purpose is to estimate the
dimensioning and power consumption of a fiber network infrastructure that is ideally sized to
satisfy different usage scenarios. It is tailored to cover a territory of the size of France centered
around a main IXP collocated with a unique CDN playing the role of a cache.

URL: https://limits4ict.gitlabpages.inria.fr/assessing-vod-pressure-on-network-

power-consumption/

Contact: Gael Guennebaud

Partners: Université de Bordeaux, LaBRI

7.2 New platforms

7.2.1 La Coupole

Participants:

Romain Pacanowski, Marjorie Paillet, Morgane Gerardin, David Mur-
ray.

La Coupole is a measurement device that acquires spatially-varying reflectance function (SV-BRDF)

on non-planar object which area can be up to 1.5m2.

Its main characteristics are:

• RGB 12MP Camera (100 fps)

• 6-axes Robot

• 1080 White LED

• 3D Laser Scanner Laser: 200 µ m resolution

• Maximal Measurement Area: 1.75 m2

• Optical Resolution: 50 µ m

La Coupole is capable of measuring objects with an average angular accuracy of 0.22 degrees, gener-
ates 4 terabytes of data per hour of measurement and allows the measurement of spatially varying BRDFs
(SV-BRDF) on a non-planar shape. The 50 mm lens combined with the camera allows to obtain a spatial
resolution resolution of 50 microns which corresponds approximately to the resolution of the human
visual system (for an object placed at about 20 cm from the observer). After two years of conception
and realization, la Coupole was used to digitize 10 garments from the collections (cf. Figure 10) of the
Ethnography Museum of Bordeaux as well as as well as a reconstruction of an alabaster (cf. Figure 9)
within the framework of the project LaBeX "Albâtres" project and will continue to be used the project
VESPAA.

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Figure 8: Left, view of La Coupole and the robot with the 3D scanner mounted, before the acquisition
phase of the optical properties performed with a high speed camera (Right) that photographs the object
for each lit LED. (Photo Credits Arthur Pequin.)

7.2.2 Measuring Bidirectional Subscattering Reflectance and Transmission Functions

Participants: Morgane Gerardin, Romain Pacanowski.

A new platform (cf. Figure 11) to measure the volumetric properties of the light is currently being
developed. The project started in March 2022 with the arrival of our new postdoctoral fellow Morgane
Gérardin. The plaform permits to measure the diffusion of the light in reflection (aka BSSRDF) but also
in Transmission (BSSTDF). A sample holder with two motorized axes orientates the sample wrt. to the
direction of incident illumination while another motorized axis rotates a camera to image the sample (cf.
Figure 12).

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Figure 9: Left, official poster of the Exhibition Textiles 3D presented to the museum of Ethnography of
Bordeaux. Center and Right, 3D digital reconstruction of an alabaster piece using data acquired by La
Coupole.

Gilet de Femme

Ceinture

Manteau

Figure 10: Numerical reconstructions with albedo (hemispherical reflectivity) per mesh vertex for
different types of clothing : Left, the woman’s silk and cotton vest (Tibet, late nineteenth century), a video
featuring other points of view viewpoints is available here. Center,partial view of a central ornament of
the apron: in navy blue quilted satin with embroidery in net representing a lion’s head, and blue, white
and red floche silk embroideries, a little yellow; black velvet facings; origin: China, 19th century. Right,
coat made of fish skins from Siberia (twentieth century), a video showing other views is available views is
availablehere.

8 New results

8.1 Analysis and Simulation

8.1.1 Material category of visual objects computed from specular image structure

Participants:

Alexandra Schmidt (Univerity of Giessen), Pascal Barla, Katja Doer-
schner (Univerity of Giessen).

Recognizing materials and their properties visually is vital for successful interactions with our environ-
ment, from avoiding slippery floors to handling fragile objects. Yet there is no simple mapping of retinal
image intensities to physical properties. In this paper [11], we investigated what image information drives
material perception by collecting human psychophysical judgements about complex glossy objects (Fig-
ure 13). Variations in specular image structure—produced either by manipulating reflectance properties

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Figure 11: The BSSxDF measurement platform. Left: Optical setup to collimate a K5600 Joker light to a
1mm2 spot on the material sample. Center: Sample holder; here alabaster. Right: Incident illumination
at 550nm wavelength focused on the material sample, revealing lateral light transport.

Figure 12: Measurement principle: a DSLR camera takes picture all around the material sample. Here
shown with a Charucco calibration target used to calibrate the camera intrinsics and extrinsics paramet-
ers.

or visual features directly—caused categorical shifts in material appearance, suggesting that specular
reflections provide diagnostic information about a wide range of material classes. Perceived material
category appeared to mediate cues for surface gloss, providing evidence against a purely feedforward
view of neural processing. Our results suggest that the image structure that triggers our perception of
surface gloss plays a direct role in visual categorization, and that the perception and neural processing of
stimulus properties should be studied in the context of recognition, not in isolation.

8.2 From Acquisition to Display

8.2.1 Depth from Focus using Windowed Linear Least Squares Regressions

Participants: Corentin Cou, Gaël Guennebaud.

We present in this paper [9] a novel depth from focus technique. Following prior work, our pipeline
starts with a focal stack and an estimation of the amount of defocus as given by, for instance, the
Ring Difference Filter. To improve robustness to outliers while avoiding to rely on costly non-linear
optimizations, we propose an original scheme that linearly scans the profile over a fixed size window,
searching for the best peak within each window using a linearized least-squares Laplace regression. As a

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Inria Annual Report 2023

Figure 13: Multiple semantic terms can describe the same qualitative visual appearance of a material. The
object in the first panel has a dark brown, lumpy surface with medium-clarity specular reflections and
could be labelled ’melted chocolate’ or ’mud’. This surface has a different visual quality from the object
in the middle panel, which has low-clarity, dim reflections that make it look like ’latex’ or ’rubber’. The
object in the third panel has very rough, anisotropic specular reflections that rapidly change in brightness
at the boundaries between highlights and lowlights, giving it the visual characteristics of ’velvet’, ’silk’ or
’fabric’.

post-process, depth estimates with low confidence are reconstructed though an adaptive Moving Least
Squares filter. We show how to objectively evaluate the performance of our approach by generating
synthetic focal stacks from which the reconstructed depth maps can be compared to ground truth
(Figure 14). Our results show that our method achieves higher accuracy than previous non-linear Laplace
regression technique, while being orders of magnitude faster.

Figure 14: Left: Acquisition setup. Right: Results on a real-word stack from an engraved stone. Top: 3D
profile of the acquired engraving area. Bottom left: blurry image obtained on the image stack. Bottom
right: reconstituted clear image of the engraving.

8.3 Rendering, Visualization and Illustration

8.3.1 A Micrograin BSDF Model for the Rendering of Porous Layers

Participants:

Simon Lucas, Mickaël Ribardière (Université de Poitiers), Ro-
main Pacanowski, Pascal Barla.

We introduce a new BSDF model for the rendering of porous layers, as found on surfaces covered by
dust, rust, dirt, or sprayed paint (Figure 15). Our approach [16] is based on a distribution of elliptical
opaque micrograins, extending the Trowbridge-Reitz (GGX) distribution to handle pores (i.e., spaces
between micrograins). We use distance field statistics to derive the corresponding Normal Distribution
Function (NDF) and Geometric Attenuation Factor (GAF), as well as a view- and light- dependent filling

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factor to blend between the porous and base layers. All the derived terms show excellent agreement when
compared against numerical simulations. Our approach has several advantages compared to previous
work. First, it decouples structural and reflectance parameters, leading to an analytical single-scattering
formula regardless of the choice of micrograin reflectance. Second, we show that the classical texture
maps (albedo, roughness, etc) used for spatially-varying material parameters are easily retargeted to work
with our model. Finally, the BRDF parameters of our model behave linearly, granting direct multi-scale
rendering using classical mip mapping.

Figure 15: Our micrograin BSDF model provides a physically-plausible solution for laying porous layers
onto an arbitrary surfaces. This is here shown for a number of materials: we use diffuse grains on a plastic
watering can to reproduce mud, copper oxide grains on a metallic bucket for rust, diffuse grains on a
transparent glass for dirt, gold grains on diffuse or aluminium vases to mimic spray paint, soot grains on
the diffuse broken pots and diffuse grains on wooden shelves.

8.4 Editing and Modeling

8.4.1 Non-linear Rough 2D Animation using Transient Embeddings

Participants: Melvin Even, Pierre Bénard, Pascal Barla.

Traditional 2D animation requires time and dedication since tens of thousands of frames need to
be drawn by hand for a typical production. Many computer-assisted methods have been proposed to
automatize the generation of inbetween frames from a set of clean line drawings, but they are all limited
by a rigid workflow and a lack of artistic controls, which is in the most part due to the one-to-one stroke
matching and interpolation problems they attempt to solve. In this work [10], we take a novel view
on those problems by focusing on an earlier phase of the animation process that uses rough drawings
(i.e., sketches). Our key idea is to recast the matching and interpolation problems so that they apply
to transient embeddings, which are groups of strokes that only exist for a few keyframes. A transient
embedding carries strokes between keyframes both forward and backward in time through a sequence of
transformed lattices. Forward and backward strokes are then cross-faded using their thickness to yield
rough inbetweens. With our approach, complex topological changes may be introduced while preserving
visual motion continuity. As demonstrated on state-of-the-art 2D animation exercises (e.g., Figure 16),
our system provides unprecedented artistic control through the non-linear exploration of movements
and dynamics in real-time.

8.4.2 Efficient Interpolation of Rough Line Drawings

Participants:

Jiazhou Chen (Zhejiang University of Technology), Xinding Zhu (Zheji-
ang University of Technology), Melvin Even, Jean Basset, Pierre Bénard,
Pascal Barla.

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Inria Annual Report 2023

Figure 16: Our rough animation system supports the inbetweening of complex special effects with
multiple topological events, here a flame splitting into smaller components. In (a), we show the input
key drawings traced over a reference from Gilland’s book, while in (b) we show their decomposition into
transient embeddings. Even though we use a unique color per embedding throughout the animation
for visualization, each embedding only exists between a pair of keyframes in this example. This allows
the handling of topological changes, which occur at the third and fifth keyframes. In (c), we display in
magenta a subset of the inbetween frames generated by our animation system in real-time.

In traditional 2D animation, sketches drawn at distant keyframes are used to design motion, yet it
would be far too labor-intensive to draw all the inbetween frames to fully visualize that motion. We
propose a novel efficient interpolation algorithm that generates these intermediate frames in the artist’s
drawing style [8]. Starting from a set of registered rough vector drawings, we first generate a large
number of candidate strokes during a pre-process, and then, at each intermediate frame, we select the
subset of those that appropriately conveys the underlying interpolated motion, interpolates the stroke
distributions of the key drawings, and introduces a minimum amount of temporal artifacts (Figure 17). In
addition, we propose quantitative error metrics to objectively evaluate different stroke selection strategies.
We demonstrate the potential of our method on various animations and drawing styles, and show its
superiority over competing raster- and vector-based methods.

Figure 17: Our interpolation technique takes as input a series of rough vector key drawings (shown in
light gray), and produces intermediate drawings (in black) that preserve the input drawing style(s). It is
based on an interactive stroke distribution synthesis algorithm that introduces a minimum amount of
temporal artifacts.

8.4.3 One-to-Many Spectral Upsampling of Reflectances and Transmittances

Participants:

Laurent Belcour (Unity Labs, Grenoble), Pascal Barla.

Spectral rendering is essential for the production of physically-plausible synthetic images, but requires
to introduce several changes in the content generation pipeline. In particular, the authoring of spectral
material properties (e.g., albedo maps, indices of refraction, transmittance coefficients) raises new
problems. While a large panel of computer graphics methods exists to upsample a RGB color to a

Project MANAO

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spectrum, they all provide a one-to-one mapping. This limits the ability to control interesting color
changes such as the Usambara effect or metameric spectra (Figure 18). In this work [7], we introduce a
one-to-many mapping in which we show how we can explore the set of all spectra reproducing a given
input color. We apply this method to different colour changing effects such as vathochromism - the
change of color with depth, and metamerism.

Figure 18: Surprising color changes may occur due to the path length travelled by light through some
materials. This is shown in (a) with the Usambara effect observed in tourmaline: the same gem appears
green or red depending on path length, due to a specific transmission spectrum (see inset). We introduce a
method for finding such non-generic spectra. This requires to perform one-to-many spectral upsampling
(b), which generates multiple spectra with a unique controllable color at a given path length, but varying
colors at further lengths. The chromaticity of each spectrum as a function of path lengths is shown in (c).

8.4.4 FlavoMetrics: Towards a Digital Tool to Understand and Tune Living Aesthetics of Flavobacteria

Participants: Clarice Risseeuw (Technical University, Delft), Jose Castro (Technical
University, Delft), Pascal Barla, Elvin Karana (Technical University,
Delft).

Integrating microorganisms into artefacts is a growing area of interest for HCI designers. However,
the time, resources, and knowledge required to understand complex microbial behaviour limits design-
ers from creatively exploring temporal expressions in living artefacts, i.e., living aesthetics. Bridging
biodesign and computer graphics, we developed FlavoMetrics [17], an interactive digital tool that sup-
ports biodesigners in exploring Flavobacteria’s living aesthetics (Figure 19). This open-source tool enables
designers to virtually inoculate bacteria and manipulate stimuli to tune Flavobacteria’s living colour
in a digital environment. Six biodesigners evaluated the tool and reflected on its implications for their
practices, for example, in (1) understanding spatio-temporal qualities of microorganisms beyond 2D, (2)
biodesign education, and (3) the experience prototyping of living artefacts. With FlavoMetrics, we hope
to inspire novel HCI tools for accessible and time- and resource-efficient biodesign as well as for better
alignment with divergent microbial temporalities in living with living artefacts.

8.4.5 ImmersiveIML - Immersive interactive machine learning for 3D point cloud classification

Participants: Maxime Cordeil (University of Queensland), Thomas Billy (Univer-
sity of Queensland), Nicolas Mellado (CNRS, IRIT, Universite de
Toulouse), Loïc Barthe (CNRS, IRIT, Universite de Toulouse), Nad-
ine Couture (Univ. Bordeaux, ESTIA Institute of Technology), Patrick Re-
uter.

We propose in this work [14] an initial exploration of an interactive machine learning (IML) dialogue
in immersive, interactive Virtual Reality (VR) for the classification of points in 3D point clouds. We

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Inria Annual Report 2023

Figure 19: Left: Real photograph of Flavobacteria; Right: Screen capture of FlavoMetrics, our interactive
digital tool that supports biodesigners in exploring Flavobacteria’s living aesthetics.

contribute ImmersiveIML, an Immersive Analytics tool which builds on humanmachine learning trial-
and-error dialogue to support an iterative classification process of points in the 3D point cloud. The
interactions in ImmersiveIML are designed to be both expressive and minimal; we designed the iterative
process to be supported by (1) 6-DOF controllers to allow direct interaction in large 3D point clouds
via a minimal brushing technique, (2) a fast trainable machine learning model, and (3) the direct visual
feedback of classification results.

This constitutes an iterative human-in-the-loop process that eventually converges to a classification
according to human intent. We argue that this approach is a novel contribution that supports a constant
improvement of the classification model and fast tracks classification tasks with this type of data, in an
immersive scenario. We report on the design and implementation of ImmersiveIML and demonstrate its
capabilities with two emblematic application scenarios: edge detection in 3D and classification of trees
in a city LiDAR dataset.

Figure 20: Starting from a human intent, the iterative human-in-the-loop process is composed of (1)
6-DOF controllers to allow direct interaction in large 3D point clouds via a minimal brushing technique,
(2) a fast trainable machine learning model, and (3) the direct visual feedback of classification results. In
the shown iteration, the classification result does not correspond to the user’s intent, so they refine the
input to eventually converge to an acceptable classification of the data, iteratively.

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8.5 Modeling environnemental impacts of digitization

Participants: Gaël Guennebaud, Aurélie Bugeau (Université de Bordeaux, LaBRI).

Assessing the energy consumption or carbon footprint of data distribution of video streaming services
is usually carried out through energy or carbon intensity figures (in Wh or gCO2e per GB). In a ICT4S
paper [15] we reviewed the reasons why such approaches are likely to lead to misunderstandings and
potentially to erroneous conclusions. To overcome those shortcomings, we designed a new methodology
whose key idea is to consider a video streaming usage at the whole scale of a territory, and evaluate the
impact of this usage on the network infrastructure. At the core of our methodology is a parametric model
of a simplified network and Content Delivery Network (CDN) infrastructure, which is automatically scaled
according to peak usage needs. This allows us to compare the power consumption of this infrastructure
under different scenarios, ranging from a sober baseline to a generalized use of high bitrate videos. Our
results show that classical efficiency indicators do not reflect the power consumption increase of more
intensive Internet usage, and might even lead to misleading conclusions. This makes our approach
more suitable to drive actions and study “What-if” scenarios. Our model is publicly available as a web
application illustrated in Figure 21.

Figure 21: Illustration of our simplified parametric model of a network with estimated power consumption
for different video qualities and baseline network settings.

9 Bilateral contracts and grants with industry

9.1 Bilateral contracts with industry

Research collaboration contract with Praxinos (2021-2024)

Participants: Melvin Even, Pierre Bénard, Pascal Barla.

This collaboration aims at defining new computer-assisted tools to facilitate the creation of 2D anim-

ations.

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Inria Annual Report 2023

CIFRE PhD contract with Ubisoft (2022-2025)

Participants:

Panagiotis Tsiapkolis, Pierre Bénard.

In this project, we aim at exploring artistic control in real-time stylized rendering of 3D scenes.

10 Partnerships and cooperations

10.1 International initiatives

10.1.1 Visits to international teams

Research stays abroad Patrick Reuter

Visited institutions: Monash University (Department of Human-Centred Computing) and University of

Queensland (Human-Centred Computing team)

Country: Australia

Dates: September 2022 - August 2023

Context of the visit: CRCT

Type of mobility: research stay

The primary objective of this research stay is to foster collaborations and explore interactive machine

learning approaches for geometry processing.

10.2 National initiatives

10.2.1 ANR

“Young Researcher” MoStyle (2021-2025)

- INRIA - LaBRI

Participants:

Pierre Bénard, Pascal Barla, Melvin Even.

The main goal of this project is to investigate how computer tools can help capturing and reproducing
the typicality of traditional 2D animations. Ultimately, this would allow to produce 2D animations with a
unified appearance starting from roughs drawings or 3D inputs.

10.2.2 Inria Exploratory actions

Exploratory Action Ecoptics (2021-2024)

- INRIA

Participants: Gary Fourneau, Pascal Barla, Romain Pacanowski.

The main topic of this project is to contribute to the understanding of visual communication among
animals. It explores new computer graphics techniques Il se propose de déveloper de nouvelles tech-
niques de synthèse d’images afin de combler le fossé qui sépare les approches optique et écologique,
connectant ainsi le transport lumineux dans des structures microscopiques à la surface d’un animal, aux
signaux visuels perceptibles par un autre animal dans son habitat.

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10.3 Regional initiatives

Vespaa (2022-2027)

- INRIA and Region Nouvelle Aquitaine

Participants: Romain Pacanowski, Pierre Bénard, Pascal Barla, Gael Guennebaud,

Pierre Mézière.

The project VESPAA focuses on high-fidelity rendering of African artifacts stored in different museums

(Angoulême, La Rochelle, Bordeaux) of the Nouvelle Aquitaine Region.

The main goals of VESPAA are:

• To improve the quality of measurements data acquired by La Coupole in order to represent complex

materials (gilding, pearl, patina and varnish)

• To develop new SV-BRDF models and multi-resolution representations to visualize in real-time
and with high fidelity the digitized objects under dynamic light and view directions. The targeted
audience ranges from Cultural Heritage experts to mainstream public visiting the museums.

• To make durable the acquired reflectance properties by storing the measurements (geometry and

tabulated data) as well as the SV-BRDF models (Source Code) in a open database.

11 Dissemination

11.1 Promoting scientific activities

Reviewer

• ACM Siggraph, ACM Siggraph Asia

• Eurographics

• Eurographics Symposium on Rendering

• ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (i3D)

• ACM Symposium on User Interface Software and Technology (UIST)

11.1.1 Journal

• IEEE Transactions on Visualization and Computer Graphics

• Journal of Industrial Ecology

• IPOL

11.1.2 Invited talks

• Appamat Workshop on Wet Materials (Pascal Barla).

11.1.3 Scientific expertise

• Gaël Guennebaud is providing expertize within two working groups of TheShiftProject on suffi-
ciency in future Internet mobile network and on the role and impact of technologies within low
GHG emission and resilient agricultural scenarios.

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Inria Annual Report 2023

11.2 Teaching - Supervision - Juries

11.2.1 Teaching

The members of our team are involved in teaching computer science at University of Bordeaux and
ENSEIRB-Matmeca engineering School. General computer science is concerned, as well as the following
graphics related topics:

• Master: Pierre Bénard, Gaël Guennebaud, Romain Pacanowski, Advanced Image Synthesis, 50 HETD,

M2, Univ. Bordeaux, France.

• Master: Gaël Guennebaud, Pierre Bénard and Simon Lucas, 3D Worlds, 60 HETD, M1, Univ. Bor-

deaux and IOGS, France.

• Master: Pierre Bénard, Virtual Reality, 20 HETD, M2, Univ. Bordeaux, France.

• Master: Romain Pacanowski, 3D Programming, M2, 18 HETD, Bordeaux INP ENSEIRB Matmeca,

France.

• Licence: Gaël Guennebaud and Pierre La Rocca, Introduction to climate change issues and the

environnemental impacts of ICT, 24 HETD, L3, Univ. Bordeaux, France.

One member is also in charge of a field of study:

• Master: Pierre Bénard, M2 “Informatique pour l’Image et le Son”, Univ. Bordeaux, France.

11.2.2 Supervision

• PhD: Corentin Cou, “Characterization of visual appearance for 3D restitution and exploration
of monumental heritage”, Inria & IOGS & CNRS/INHA, G. Guennebaud, X. Granier, M. Volait, R.
Pacanowski. 08/12/2024

• PhD in progress: Melvin Even, “Computer-Aided Rough 2D Animation”, Inria & Univ. Bordeaux, P.

Barla, P. Bénard

• PhD in progress: Gary Fourneau, “ Modélisation de l’Apparence Visuelle pour l’Écologie Comporte-

mentale”, Inria & Univ. Bordeaux, P. Barla, R. Pacanowski

• PhD in progress: Simon Lucas, “Modélisation de l’apparence et rendu des plantes”, Inria & Univ.

Bordeaux, P. Barla, R. Pacanowski

• PhD in progress: Panagiotis Tsiapkolis, “Study of Artistic Control in Real-Time Expressive Render-

ing”, Inria & Ubisoft, P. Bénard

• PhD in progress: Pierre La Rocca, “Modélisation paramétrique des impacts environnementaux des
TIC : focus sur la numérisation de l’agriculture.”, Univ. Bordeaux, Aurélie Bugeau, G. Guennebaud

11.2.3 Juries

Phd Thesis

- Fanny Dailliez (Université Grenoble Alpes): Multiscale impact of the addition of a coating layer on

the color of halftone prints. PhD Defense Date: October 19th 2023. Jury:

- Carinna PARRAMAN, Reviewer (CFPR)

- Romain Pacanowski Reviewer (Inria)

- Clotilde BOUST, Examiner (C2RMF)

- Evelyne MAURET, Examiner (LGP2)

- Anne BLAYO, Director (LGP2)

- Lionel CHAGAS, Supervisor, (LGP2)

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27

- Thierry FOURNEL, Co-director, (Lab. Hubert Curien)

- Mathieu HEBERT, Supervisor, (Lab. Hubert Curien)

- Vincent Duveiller (Université Jean Monnet, IOGS): Optical models for predicting the color of dental

composite materials. PhD Defense Date: Jury:

• GHINEA Razvan, Reviewer (University of Granada, Facultad de Ciencia)

• PILLONNET Anne, Reviewer (Université Claude Bernard Lyon 1, ILM)

• ANDRAUD Christine, Examiner (Muséum National d’Histoire Naturelle)

• PACANOWSKI Romain, Examiner (Inria)

• VYNCK Kevin, Examiner (CNRS)

• HEBERT Mathieu, Director (Université Jean Monnet, IOGS)

• CLERC Raphaël, Co-director (Université Jean Monnet, IOGS)

• SALOMON Jean-Pierre, invited (Université de Lorraine, Faculté de Nancy)

• JOST Sophie, invited (ENTPE)

11.3 Popularization

• During the national “Semaine des maths” event at INRIA (06/03/2023), Pierre Bénard gave a 45

minutes talk titled L’art et la science des films d’animation 3D.

12 Scientific production

12.1 Major publications

[1] L. Belcour and P. Barla. ‘A Practical Extension to Microfacet Theory for the Modeling of Varying
Iridescence’. In: ACM Transactions on Graphics 36.4 (July 2017), p. 65. DOI: 10.1145/3072959.30
73620. URL: https://hal.archives-ouvertes.fr/hal-01518344.

[2] T. Crespel, P. Reuter, A. Travis, Y. Gentet and X. Granier. ‘Autostereoscopic transparent display
using a wedge light guide and a holographic optical element: implementation and results’. In:
Applied optics 58.34 (Nov. 2019). DOI: 10.1364/AO.58.00G293. URL: https://hal.inria.fr
/hal-02352672.

[3] N. Holzschuch and R. Pacanowski. ‘A Two-Scale Microfacet Reflectance Model Combining Re-
flection and Diffraction’. In: ACM Transactions on Graphics 36.4 (July 2017). Article 66, p. 12. DOI:
10.1145/3072959.3073621. URL: https://hal.inria.fr/hal-01515948.

[4] T. Lambert, P. Bénard and G. Guennebaud. ‘A View-Dependent Metric for Patch-Based LOD Gen-
eration & Selection’. In: Proceedings of the ACM on Computer Graphics and Interactive Techniques
1.1 (May 2018). DOI: 10.1145/3203195. URL: https://hal.inria.fr/hal-01779816.

[5] G. Nader and G. Guennebaud. ‘Instant Transport Maps on 2D Grids’. In: ACM Transactions on
Graphics 37.6 (Nov. 2018), p. 13. DOI: 10.1145/3272127.3275091. URL: https://hal.inria.f
r/hal-01884157.

[6] A. Schmid, P. Barla and K. Doerschner. ‘Material category of visual objects computed from specular
image structure’. In: Nature Human Behaviour 7.7 (July 2023), pp. 1152–1169. DOI: 10.1038/s41
562-023-01601-0. URL: https://inria.hal.science/hal-04173993.

12.2 Publications of the year

International journals

[7] L. Belcour, P. Barla and G. Guennebaud. ‘One-to-Many Spectral Upsampling of Reflectances and
Transmittances’. In: Computer Graphics Forum 42.4 (July 2023). DOI: 10.1111/cgf.14886. URL:
https://inria.hal.science/hal-04174001.

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Inria Annual Report 2023

[8]

J. Chen, X. Zhu, M. Even, J. Basset, P. Bénard and P. Barla. ‘Efficient Interpolation of Rough Line
Drawings’. In: Computer Graphics Forum 42.7 (2023). DOI: 10.1111/cgf.14946. URL: https:
//inria.hal.science/hal-04202841.

[9] C. Cou and G. Guennebaud. ‘Depth from Focus using Windowed Linear Least Squares Regressions’.
In: The Visual Computer (Mar. 2023). DOI: 10.1007/s00371-023-02841-x. URL: https://inri
a.hal.science/hal-04052197.

[10] M. Even, P. Bénard and P. Barla. ‘Non-linear Rough 2D Animation using Transient Embeddings’.
In: Computer Graphics Forum (May 2023). DOI: 10.1111/cgf.14771. URL: https://inria.hal
.science/hal-04006992.

[11] A. Schmid, P. Barla and K. Doerschner. ‘Material category of visual objects computed from specular
image structure’. In: Nature Human Behaviour 7.7 (July 2023), pp. 1152–1169. DOI: 10.1038/s41
562-023-01601-0. URL: https://inria.hal.science/hal-04173993.

International peer-reviewed conferences

[12] A. Agreda, A. Hereu, R. Pacanowski, T. Wu, A. Dufay, X. Granier, M. Tréguer-Delapierre, G. L. Drisko,
K. Vynck and P. Lalanne. ‘Tailoring the visual appearance with disordered arrays of resonant
metaatoms’. In: MOC 2023 - 28th Microoptics Conference. Miyazaki, Japan: IEEE, 27th Sept. 2023,
pp. 1–2. DOI: 10.23919/MOC58607.2023.10302921. URL: https://hal.science/hal-04282
970.

[13] V. Baillet, P. Mora, C. Cou, X. Granier and M. Volait. ‘3D Restitution of Saint-Maurice Residence in
Cairo – 3D as a tool to monitor and study architectural reuses’. In: Interiors reconfigured: changing
Materiality & craftsmanship in the decorative arts of the Middle East & North Africa (18th–20th
centuries). Vitrocentre Romont, Switzerland, 3rd Nov. 2023. URL: https://hal.science/hal-0
3933618.

[14] M. Cordeil, T. Billy, N. Mellado, L. Barthe, N. Couture and P. Reuter. ‘ImmersiveIML – Immersive
interactive machine learning for 3D point cloud classification: the neural network at your finger-
tips’. In: ISMAR 2023 - IEEE International Symposium on Mixed and Augmented Reality Adjunct.
IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct 2023).
Sydney, Australia: IEEE, 16th Oct. 2023, à paraître. URL: https://hal.science/hal-04246601.

[15] G. Guennebaud, A. Bugeau and A. Dudouit. ‘Assessing VoD pressure on network power consump-
tion’. In: ICT4S - International Conference on Information and Communications Technology for
Sustainability. Rennes, France, 5th June 2023. URL: https://hal.science/hal-04059523.

[16]

S. Lucas, M. Ribardière, R. Pacanowski and P. Barla. ‘A Micrograin BSDF Model for the Rendering
of Porous Layers’. In: Siggraph Asia 2023 - The 16th ACM SIGGRAPH Conference and Exhibition
on Computer Graphics and Interactive Techniques in Asia. Sydney, Australia, 12th Dec. 2023. URL:
https://hal.science/hal-04220006.

[17] C. Risseeuw, J. F. Martinez Castro, P. Barla and E. Karana. ‘FlavoMetrics: Towards a Digital Tool
to Understand and Tune Living Aesthetics of Flavobacteria’. In: DIS 2023 - Designing Interactive
Systems Conference. Pittsburgh PA, United States: ACM, 10th July 2023, pp. 2079–2092. DOI:
10.1145/3563657.3596085. URL: https://inria.hal.science/hal-04173998.

Conferences without proceedings

[18] A. Agreda, T. Wu, A. Hereu, R. Pacanowski, A. Dufay, X. Granier, M. Tréguer-Delapierre, G. L.
Drisko, K. Vynck and P. Lalanne. ‘Control of visual appearance with disordered plasmonic resonant
metasurfaces’. In: SPP 2023 - The International Conference on Surface Plasmon Photonics 10.
Houston, TX„ France, 21st May 2023. URL: https://hal.science/hal-04177866.

[19] C. Cou, H. de La Codre, X. Granier and A. Mounier. ‘Pearson correlation-based method on hyper-
spectral images for the study of similarity of pigments and dyes’. In: International conference on
analytical techniques in art and cultural heritage - TECHNART 2023 LISBON. Lisbonne, Portugal,
7th May 2023. URL: https://hal.science/hal-04033425.

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[20] C. Cou, R. Pacanowski and X. Granier. ‘In-situ and highly portable SVBRDF acquisition method’.
In: IS&T/Appamat International Workshop on Material Appearance. Paris, France, 13th Nov. 2023.
URL: https://hal.science/hal-04291656.

Scientific book chapters

[21] P. Barla, L. Lachiver and G. Guennebaud. ‘Visual Effects of Surface Tension on the Appearance of
Wet Surfaces’. In: RADIOMETRY OF WET SURFACES - When Water Matters. 2nd Feb. 2023. URL:
https://inria.hal.science/hal-03977621.

Doctoral dissertations and habilitation theses

[22] C. Cou. ‘New approach of 3D for monumental heritage’. Université de Bordeaux, 8th Dec. 2023.

URL: https://hal.science/tel-04364363.

[23] R. Pacanowski. ‘Toward a complete predictive rendering pipeline for numerical prototyping and
cultural heritage applications’. Université de Bordeaux (UB), 21st June 2023. URL: https://inri
a.hal.science/tel-04380582.

Reports & preprints

[24] A. Bugeau, N. Bonichon and G. Guennebaud. Cours Sensibilisation à l’écologie et à l’impact du
numérique - UF Informatique - Université de Bordeaux. May 2023. URL: https://hal.science
/hal-04219918.

[25] L. Lefèvre, A.-L. Ligozat, D. Trystram, S. Bouveret, A. Bugeau, J. Combaz, E. Frenoux, G. Guen-
nebaud, J. Lefèvre, J.-P. Nicolaï and K. Dassas. Environmental assessment of projects involving AI
methods. 4th Jan. 2023. URL: https://hal.science/hal-03922093.

[26] P. L. Rocca. Towards a methodology to consider the environmental impacts of digital agriculture.

2023. URL: https://hal.science/hal-04081280.

12.3 Other

Softwares

[27]

[SW ] M. Even, P. Bénard and P. Barla, Frite, 11th Sept. 2023. LIC: CeCILL Free Software License
Agreement v2.1; GNU General Public License v2.0 or later; GNU General Public License v3.0 or
later; Mozilla Public License 2.0. HAL: 〈hal-04202866〉, URL: https://inria.hal.science/h
al-04202866, VCS: https://github.com/MoStyle/frite, SWHID: 〈swh:1:dir:ef9692ba22
458dbde3e34d5bd6381413a7cdfe78;origin=https://github.com/MoStyle/frite;visi
t=swh:1:snp:ba28256b9657c8a69f0e14c97155a51f408d9f3b;anchor=swh:1:rev:5a754
964f493da399f0833cea82ef38b5e523bb0〉.

12.4 Cited publications

[28] M. Alexa and W. Matusik. ‘Reliefs as images’. In: ACM Trans. Graph. 29.4 (2010). DOI: http://doi
.acm.org/10.1145/1778765.1778797. URL: http://doi.acm.org/10.1145/1778765.177
8797.

[29] L. Ammann, P. Barla, X. Granier, G. Guennebaud and P. Reuter. ‘Surface Relief Analysis for Illus-
trative Shading’. In: Computer Graphics Forum 31.4 (June 2012), pp. 1481–1490. DOI: 10.1111/j
.1467-8659.2012.03144.x. URL: http://hal.inria.fr/hal-00709492.

[30] M. Ashikhmin and S. Premoze. Distribution-based BRDFs. Tech. rep. unpublished. Univ. of Utah,

2007.

[31] B. Atcheson, I. Ihrke, W. Heidrich, A. Tevs, D. Bradley, M. Magnor and H.-P. Seidel. ‘Time-resolved

3D Capture of Non-stationary Gas Flows’. In: ACM Trans. Graph. 27.5 (2008).

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[32] O. Bimber and R. Raskar. Spatial Augmented Reality: Merging Real and Virtual Worlds. A K

Peters/CRC Press, 2005.

[33] A. Bousseau, E. Chapoulie, R. Ramamoorthi and M. Agrawala. ‘Optimizing Environment Maps for
Material Depiction’. In: Comput. Graph. Forum (Proceedings of the Eurographics Symposium on
Rendering) 30.4 (2011). URL: http://www-sop.inria.fr/reves/Basilic/2011/BCRA11.

[34]

S. Boyé, G. Guennebaud and C. Schlick. ‘Least Squares Subdivision Surfaces’. In: Comput. Graph.
Forum 29.7 (2010), pp. 2021–2028. URL: http://hal.inria.fr/inria-00524555/en.

[35] S. Bruckner and M. E. Gröller. ‘Style Transfer Functions for Illustrative Volume Rendering’. In:
Comput. Graph. Forum 26.3 (2007), pp. 715–724. URL: http://www.cg.tuwien.ac.at/resear
ch/publications/2007/bruckner-2007-STF/.

[36] C. Buehler, M. Bosse, L. McMillan, S. Gortler and M. Cohen. ‘Unstructured lumigraph rendering’.
In: Proc. ACM SIGGRAPH. 2001, pp. 425–432. DOI: http://doi.acm.org/10.1145/383259.383
309. URL: http://doi.acm.org/10.1145/383259.383309.

[37] O. Cakmakci, S. Vo, K. P. Thompson and J. P. Rolland. ‘Application of radial basis functions to
shape description in a dual-element off-axis eyewear display: Field-of-view limit’. In: J. Society for
Information Display 16.11 (2008), pp. 1089–1098. DOI: 10.1889/JSID16.11.1089.

[38] E. Cerezo, F. Perez-Cazorla, X. Pueyo, F. Seron and F. X. Sillion. ‘A Survey on Participating Media
Rendering Techniques’. English. In: The Visual Computer (2005). URL: http://hal.inria.fr/i
nria-00510151.

[39]

J. Chen, X. Granier, N. Lin and Q. Peng. ‘On-Line Visualization of Underground Structures using
Context Features’. In: Symposium on Virtual Reality Software and Technology (VRST). ACM. 2010,
pp. 167–170. DOI: 10.1145/1889863.1889898. URL: http://hal.inria.fr/inria-0052481
8/en.

[40]

J. Chen, G. Guennebaud, P. Barla and X. Granier. ‘Non-oriented MLS Gradient Fields’. In: Computer
Graphics Forum (Dec. 2013). URL: http://hal.inria.fr/hal-00857265.

[41] O. Cossairt, S. Nayar and R. Ramamoorthi. ‘Light field transfer: global illumination between real
and synthetic objects’. In: ACM Trans. Graph. 27.3 (2008). DOI: http://doi.acm.org/10.1145
/1360612.1360656. URL: http://doi.acm.org/10.1145/1360612.1360656.

[42] F. Cuny, L. Alonso and N. Holzschuch. ‘A novel approach makes higher order wavelets really

efficient for radiosity’. In: Comput. Graph. Forum 19.3 (2000), pp. 99–108.

[43] E. D’Eon and G. Irving. ‘A quantized-diffusion model for rendering translucent materials’. In:
ACM Trans. Graph. 30.4 (2011). DOI: http://doi.acm.org/10.1145/2010324.1964951. URL:
http://doi.acm.org/10.1145/2010324.1964951.

[44] K. J. Dana, B. van Ginneken, S. Nayar and J. J. Koenderink. ‘Reflectance and texture of real-world
surfaces’. In: IEEE Conference on Computer Vision and Pattern Recognition (ICCV). 1997, pp. 151–
157.

[45] Y. Dong, J. Wang, F. Pellacini, X. Tong and B. Guo. ‘Fabricating spatially-varying subsurface scatter-
ing’. In: ACM Trans. Graph. 29.4 (2010). DOI: http://doi.acm.org/10.1145/1778765.177879
9. URL: http://doi.acm.org/10.1145/1778765.1778799.

[46] P. Dutré, K. Bala and P. Bekaert. Advanced Global Illumination. A.K. Peters, 2006.

[47] K. Egan, F. Hecht, F. Durand and R. Ramamoorthi. ‘Frequency analysis and sheared filtering for
shadow light fields of complex occluders’. In: ACM Trans. Graph. 30 (2 2011). DOI: http://doi.a
cm.org/10.1145/1944846.1944849. URL: http://doi.acm.org/10.1145/1944846.19448
49.

[48] K. Egan, Y.-T. Tseng, N. Holzschuch, F. Durand and R. Ramamoorthi. ‘Frequency Analysis and
Sheared Reconstruction for Rendering Motion Blur’. In: ACM Trans. Graph. 28.3 (2009). DOI:
10.1145/1531326.1531399. URL: http://hal.inria.fr/inria-00388461/en.

[49] R. Fleming, A. Torralba and E. H. Adelson. ‘Specular reflections and the perception of shape’. In: J.
Vis. 4.9 (2004), pp. 798–820. eprint: http://journalofvision.org/4/9/10/Fleming-2004-j
ov-4-9-10.pdf. URL: http://journalofvision.org/4/9/10/.

Project MANAO

31

[50] A. Ghosh, S. Achutha, W. Heidrich and M. O’Toole. ‘BRDF Acquisition with Basis Illumination’. In:

IEEE International Conference on Computer Vision (ICCV). 2007, pp. 1–8.

[51] M. Goesele, X. Granier, W. Heidrich and H.-P. Seidel. ‘Accurate Light Source Acquisition and
Rendering’. In: ACM Trans. Graph. 22.3 (2003), pp. 621–630. DOI: 10.1145/882262.882316. URL:
http://hal.inria.fr/hal-00308294.

[52] C. M. Goral, K. E. Torrance, D. P. Greenberg and B. Battaile. ‘Modeling the interaction of light

between diffuse surfaces’. In: Proc. ACM SIGGRAPH. 1984, pp. 213–222.

[53] M. Hasan, M. Fuchs, W. Matusik, H. Pfister and S. Rusinkiewicz. ‘Physical reproduction of materials
with specified subsurface scattering’. In: ACM Trans. Graph. 29.4 (2010). DOI: http://doi.acm.o
rg/10.1145/1778765.1778798. URL: http://doi.acm.org/10.1145/1778765.1778798.

[54] P. S. Heckbert. ‘Simulating Global Illumination Using Adaptative Meshing’. PhD thesis. University

of California, 1991.

[55] M. B. Hullin, M. Fuchs, I. Ihrke, H.-P. Seidel and H. P. A. Lensch. ‘Fluorescent immersion range
scanning’. In: ACM Trans. Graph. 27.3 (2008). DOI: http://doi.acm.org/10.1145/1360612.1
360686. URL: http://doi.acm.org/10.1145/1360612.1360686.

[56] M. B. Hullin, J. Hanika, B. Ajdin, H.-P. Seidel, J. Kautz and H. P. A. Lensch. ‘Acquisition and analysis
of bispectral bidirectional reflectance and reradiation distribution functions’. In: ACM Trans.
Graph. 29 (4 2010). DOI: http://doi.acm.org/10.1145/1778765.1778834. URL: http://doi
.acm.org/10.1145/1778765.1778834.

[57] M. B. Hullin, H. P. A. Lensch, R. Raskar, H.-P. Seidel and I. Ihrke. ‘Dynamic Display of BRDFs’. In:
Comput. Graph. Forum 30.2 (2011), pp. 475–483. DOI: 10.1111/j.1467-8659.2011.01891.x.
URL: http://dx.doi.org/10.1111/j.1467-8659.2011.01891.x.

[58]

[59]

[60]

[61]

[62]

[63]

I. Ihrke, K. N. Kutulakos, H. P. A. Lensch, M. Magnor and W. Heidrich. ‘Transparent and Specular
Object Reconstruction’. In: Comput. Graph. Forum 29.8 (2010), pp. 2400–2426.

I. Ihrke, I. Reshetouski, A. Manakov, A. Tevs, M. Wand and H.-P. Seidel. ‘A Kaleidoscopic Approach
to Surround Geometry and Reflectance Acquisition’. In: IEEE Conf. Computer Vision and Pattern
Recognition Workshops (CVPRW). IEEE Computer Society, 2012, pp. 29–36.

I. Ihrke, G. Wetzstein and W. Heidrich. ‘A theory of plenoptic multiplexing’. In: IEEE Conf. Com-
puter Vision and Pattern Recognition (CVPR). oral. IEEE Computer Society, 2010, pp. 483–490.

J. T. Kajiya. ‘The rendering equation’. In: Proc. ACM SIGGRAPH. 1986, pp. 143–150. DOI: http://d
oi.acm.org/10.1145/15922.15902. URL: http://doi.acm.org/10.1145/15922.15902.

J. Kautz, P.-P. Sloan and J. Snyder. ‘Fast, arbitrary BRDF shading for low-frequency lighting using
spherical harmonics’. In: Proc. Eurographics workshop on Rendering (EGWR). Pisa, Italy, 2002,
pp. 291–296.
I. Kaya, K. P. Thompson and J. P. Rolland. ‘Edge clustered fitting grids for φ-polynomial char-
acterization of freeform optical surfaces’. In: Opt. Express 19.27 (2011), pp. 26962–26974. DOI:
10.1364/OE.19.026962. URL: http://www.opticsexpress.org/abstract.cfm?URI=oe-1
9-27-26962.

[64] M. Kolomenkin, I. Shimshoni and A. Tal. ‘Demarcating curves for shape illustration’. In: ACM Trans.
Graph. (SIGGRAPH Asia) 27 (5 2008). DOI: http://doi.acm.org/10.1145/1409060.1409110.
URL: http://doi.acm.org/10.1145/1409060.1409110.

[65] M. Levoy, Z. Zhang and I. McDowall. ‘Recording and controlling the 4D light field in a microscope
using microlens arrays’. In: J. Microscopy 235.2 (2009), pp. 144–162. DOI: 10.1111/j.1365-2818
.2009.03195.x. URL: http://dx.doi.org/10.1111/j.1365-2818.2009.03195.x.

[66]

S. Li, G. Guennebaud, B. Yang and J. Feng. ‘Predicted Virtual Soft Shadow Maps with High Quality
Filtering’. In: Comput. Graph. Forum 30.2 (2011), pp. 493–502. URL: http://hal.inria.fr/inr
ia-00566223/en.

[67] C.-K. Liang, T.-H. Lin, B.-Y. Wong, C. Liu and H. H. Chen. ‘Programmable aperture photography:
multiplexed light field acquisition’. In: ACM Trans. Graph. 27.3 (2008). DOI: http://doi.acm.or
g/10.1145/1360612.1360654. URL: http://doi.acm.org/10.1145/1360612.1360654.

32

Inria Annual Report 2023

[68] Y. Liu and X. Granier. ‘Online Tracking of Outdoor Lighting Variations for Augmented Reality with
Moving Cameras’. In: IEEE Transactions on Visualization and Computer Graphics 18.4 (Mar. 2012),
pp. 573–580. DOI: 10.1109/TVCG.2012.53. URL: http://hal.inria.fr/hal-00664943.

[69] W. Matusik and H. Pfister. ‘3D TV: a scalable system for real-time acquisition, transmission, and
autostereoscopic display of dynamic scenes’. In: ACM Trans. Graph. 23.3 (2004), pp. 814–824. DOI:
http://doi.acm.org/10.1145/1015706.1015805. URL: http://doi.acm.org/10.1145/1
015706.1015805.

[70] W. Matusik, H. Pfister, M. Brand and L. McMillan. ‘A data-driven reflectance model’. In: ACM
Trans. Graph. 22.3 (2003), pp. 759–769. DOI: http://doi.acm.org/10.1145/882262.882343.
URL: http://doi.acm.org/10.1145/882262.882343.

[71] G. Müller, J. Meseth, M. Sattler, R. Sarlette and R. Klein. ‘Acquisition, Synthesis and Rendering of
Bidirectional Texture Functions’. In: Eurographics 2004, State of the Art Reports. 2004, pp. 69–94.

[72] A. Ngan, F. Durand and W. Matusik. ‘Experimental Analysis of BRDF Models’. In: Proc. Eurographics

Symposium on Rendering (EGSR). 2005, pp. 117–226.

[73] F. E. Nicodemus, J. C. Richmond, J. J. Hsia, I. W. Ginsberg and T. Limperis. Geometrical Considera-

tions and Nomenclature for Reflectance. National Bureau of Standards, 1977.

[74] M. O’Toole and K. N. Kutulakos. ‘Optical computing for fast light transport analysis’. In: ACM

Trans. Graph. 29.6 (2010).

[75] R. Pacanowski, X. Granier, C. Schlick and P. Poulin. ‘Volumetric Vector-based Representation for
Indirect Illumination Caching’. In: J. Computer Science and Technology (JCST) 25.5 (2010), pp. 925–
932. DOI: 10.1007/s11390-010-1073-8. URL: http://hal.inria.fr/inria-00505132/en.

[76] R. Pacanowski, O. Salazar-Celis, C. Schlick, X. Granier, P. Poulin and A. Cuyt. ‘Rational BRDF’. In:
IEEE Transactions on Visualization and Computer Graphics 18.11 (Feb. 2012), pp. 1824–1835. DOI:
10.1109/TVCG.2012.73. URL: http://hal.inria.fr/hal-00678885.

[77] P. Peers, D. Mahajan, B. Lamond, A. Ghosh, W. Matusik, R. Ramamoorthi and P. Debevec. ‘Com-

pressive light transport sensing’. In: ACM Trans. Graph. 28.1 (2009).

[78] B. Petit, J.-D. Lesage, C. Menier, J. Allard, J.-S. Franco, B. Raffin, E. Boyer and F. Faure. ‘Multicamera
Real-Time 3D Modeling for Telepresence and Remote Collaboration’. In: Int. J. digital multimedia
broadcasting 2010 (2010). Article ID 247108, 12 pages. DOI: 10.1155/2010/247108. URL: http:
//hal.inria.fr/inria-00436467.

[79] R. Ramamoorthi and P. Hanrahan. ‘On the relationship between radiance and irradiance: determ-
ining the illumination from images of a convex Lambertian object’. In: J. Opt. Soc. Am. A 18.10
(2001), pp. 2448–2459.

[80] R. Ramamoorthi, D. Mahajan and P. Belhumeur. ‘A first-order analysis of lighting, shading, and
shadows’. In: ACM Trans. Graph. 26.1 (2007). DOI: http://doi.acm.org/10.1145/1189762.11
89764. URL: http://doi.acm.org/10.1145/1189762.1189764.

[81] R. Raskar and J. Tumblin. Computational Photography: Mastering New Techniques for Lenses,

Lighting, and Sensors. A K Peters/CRC Press, 2012.

[82] E. Reinhard, W. Heidrich, P. Debevec, S. Pattanaik, G. Ward and K. Myszkowski. High Dynamic
Range Imaging: Acquisition, Display and Image-Based Lighting. 2nd edition. Morgan Kaufmann
Publishers, 2010.

[83] P. Ren, J. Wang, J. Snyder, X. Tong and B. Guo. ‘Pocket reflectometry’. In: ACM Trans. Graph. 30.4

(2011).

[84] P. Reuter, G. Riviere, N. Couture, S. Mahut and L. Espinasse. ‘ArcheoTUI-Driving virtual reas-
semblies with tangible 3D interaction’. In: J. Computing and Cultural Heritage 3.2 (2010), pp. 1–13.
DOI: 10.1145/1841317.1841319. URL: http://hal.inria.fr/hal-00523688/en.

[85] H. E. Rushmeier and K. E. Torrance. ‘The zonal method for calculating light intensities in the
presence of a participating medium’. In: ACM SIGGRAPH Comput. Graph. 21.4 (1987), pp. 293–302.
DOI: http://doi.acm.org/10.1145/37402.37436. URL: http://doi.acm.org/10.1145/37
402.37436.

Project MANAO

33

[86]

S. Rusinkiewicz. ‘A New Change of Variables for Efficient BRDF Representation’. In: Proc. EGWR
’98. 1998, pp. 11–22.

[87] P. Schröder and W. Sweldens. ‘Spherical wavelets: efficiently representing functions on the sphere’.
In: Proc. ACM SIGGRAPH. annual conference on Computer graphics and interactive techniques.
1995, pp. 161–172. URL: http://doi.acm.org/10.1145/218380.218439.

[88] F. X. Sillion and C. Puech. Radiosity and Global Illumination. Morgan Kaufmann Publishers, 1994.

[89] C. Soler, K. Subr, F. Durand, N. Holzschuch and F. X. Sillion. ‘Fourier Depth of Field’. In: ACM
Transactions on Graphics 28.2 (2009). DOI: 10.1145/1516522.1516529. URL: http://hal.inri
a.fr/inria-00345902.

[90] M. Stamminger, A. Scheel, X. Granier, F. Perez-Cazorla, G. Drettakis and F. X. Sillion. ‘Efficient
Glossy Global Illumination with Interactive Viewing’. In: Computer Graphics Forum 19.1 (2000),
pp. 13–25.

[91] M. Stark, J. Arvo and B. Smits. ‘Barycentric Parameterizations for Isotropic BRDFs’. In: IEEE Trans.
Vis. and Comp. Graph. 11.2 (2005), pp. 126–138. URL: http://dx.doi.org/10.1109/TVCG.200
5.26.

[92] W. Thompson, R. Fleming, S. Creem-Regehr and J. K. Stefanucci. Visual Perception from a Com-

puter Graphics Perspective. A K Peters/CRC Press, 2011.

[93] Y.-T. Tsai and Z.-C. Shih. ‘All-Frequency Precomputed Radiance Transfer Using Spherical Radial
Basis Functions and Clustered Tensor Approximation’. In: ACM Trans. Graph. 25.3 (2006), pp. 967–
976.

[94] D. Vanderhaeghe, R. Vergne, P. Barla and W. Baxter. ‘Dynamic Stylized Shading Primitives’. In: Proc.
Int. Symposium on Non-Photorealistic Animation and Rendering (NPAR). ACM, 2011, pp. 99–104.
URL: http://hal.inria.fr/hal-00617157/en.

[95] P. Vangorp, J. Laurijssen and P. Dutré. ‘The influence of shape on the perception of material
reflectance’. In: ACM Trans. Graph. 26.3 (2007). DOI: http://doi.acm.org/10.1145/1276377
.1276473. URL: http://doi.acm.org/10.1145/1276377.1276473.

[96] E. Veach and L. J. Guibas. ‘Metropolis light transport’. In: Proc. SIGGRAPH ’97. annual conference
on Computer graphics and interactive techniques. ACM/Addison-Wesley Publishing Co., 1997,
pp. 65–76. URL: http://doi.acm.org/10.1145/258734.258775.

[97] R. Vergne, P. Barla, R. Fleming and X. Granier. ‘Surface Flows for Image-based Shading Design’. In:

ACM Trans. Graph. 31.3 (2012). URL: http://hal.inria.fr/hal-00702280.

[98] R. Vergne, R. Pacanowski, P. Barla, X. Granier and C. Schlick. ‘Improving Shape Depiction under
Arbitrary Rendering’. In: IEEE Trans. Visualization and Computer Graphics 17.8 (2011), pp. 1071–
1081. DOI: 10.1109/TVCG.2010.252. URL: http://hal.inria.fr/inria-00585144/en.

[99] R. Vergne, R. Pacanowski, P. Barla, X. Granier and C. Schlick. ‘Light warping for enhanced surface
depiction’. In: ACM Trans. Graph. 28.3 (2009). front cover, 25:1. DOI: http://doi.acm.org/10.1
145/1531326.1531331. URL: http://doi.acm.org/10.1145/1531326.1531331.

[100] R. Vergne, D. Vanderhaeghe, J. Chen, P. Barla, X. Granier and C. Schlick. ‘Implicit Brushes for
Stylized Line-based Rendering’. In: Comput. Graph. Forum 30.2 (2011). 3rd best paper award,
pp. 513–522. DOI: 10.1111/j.1467-8659.2011.01892.x. URL: http://hal.inria.fr/inri
a-00569958/en.

[101] F. Vincent, L. Barthe, G. Guennebaud and M. Paulin. ‘Soft Textured Shadow Volume’. In: Comput.

Graph. Forum 28.4 (2009), pp. 1111–1120. URL: http://hal.inria.fr/inria-00390534/en.

[102]

J. Wang, Y. Dong, X. Tong, Z. Lin and B. Guo. ‘Kernel Nyström method for light transport’. In: ACM
Trans. Graph. 28.3 (2009).

[103] B. Yang, J. Feng, G. Guennebaud and X. Liu. ‘Packet-based Hierarchical Soft Shadow Mapping’. In:
Comput. Graph. Forum 28.4 (2009), pp. 1121–1130. URL: http://hal.inria.fr/inria-003905
41/en.
<scratchpad>
- Main topic: Research Centre at the University of Bordeaux
- Key points:
  - Study of interactions between light, shape, and matter
  - Development of new analysis tools and numerical models
  - Creation of new acquisition and display technologies
  - Focus on real-time methods and human visual system
  - Research on material appearance and rendering
- Structure: The document is organized into several sections, including activity report, project team, research program, application domains, social and environmental responsibility, highlights of the year, new software, and partnerships and cooperations.
- Potential artifacts to ignore: Web-related elements such as links, HTML tags, and unnecessary text.
</scratchpad>

<final_summary>
The Inria Centre at the University of Bordeaux is a research centre that focuses on the study of interactions between light, shape, and matter. The centre aims to develop new analysis tools and numerical models to improve the understanding of these interactions and create new acquisition and display technologies. The research programme is organized into four axes: analysis and simulation, acquisition and display, rendering and visualization, and editing and modeling. The centre also collaborates with industry partners and explores new software and platforms, including the development of new display technologies and the creation of open data. The research centre's goal is to improve the convergence of the real and virtual worlds through visual signals and to develop new tools for artistic control in real-time stylized rendering of 3D scenes.</final_summary>
The Inria Centre at the University of Bordeaux is a research centre that focuses on the study of interactions between light, shape, and matter. The centre aims to develop new analysis tools and numerical models to improve the understanding of these interactions and create new acquisition and display technologies. The research programme is organized into four axes: analysis and simulation, acquisition and display, rendering and visualization, and editing and modeling. The centre also collaborates with industry partners and explores new software and platforms, including the development of new display technologies and the creation of open data. The research centre's goal is to improve the convergence of the real and virtual worlds through visual signals and to develop new tools for artistic control in real-time stylized rendering of 3D scenes.