RESEARCH CENTRE

Inria Centre
at Université Côte d’Azur

2023
ACTIVITY REPORT

Project-Team

TITANE

Geometric Modeling of 3D Environments

DOMAIN

Perception, Cognition and Interaction

THEME

Interaction and visualization

Contents

Project-Team TITANE

1 Team members, visitors, external collaborators

2 Overall objectives

2.1 General Presentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 Research program

3.1 Context . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3 Approximation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.4 Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

4 Application domains

5 Highlights of the year

5.1 Awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6 New software, platforms, open data

6.1 New software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.1 CGAL - NURBS meshing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.2 3D_Roof_Modeler
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.3 ASIP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.4 BSPslicer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.5 GoCoPP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
6.1.6 Feature-Preserving Offset Mesh Generation from Topology-Adapted Octrees . . . . .
6.1.7 PlaneRegul . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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7 New results

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7.1 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
7.1.1 Sharp Feature Consolidation from Raw 3D Point Clouds via Displacement Learning
7.1.2 BPNet: Bézier Primitive Segmentation on 3D Point Clouds
9
. . . . . . . . . . . . . . .
7.1.3 KIBS: 3D Detection of Planar Roof Sections from a Single Satellite Image . . . . . . . 10
. . . . . . . . . . . . 11
7.1.4 Line-segment detection in images with fitting tolerance control
7.1.5 Registration for Urban Modeling Based on Linear and Planar Features
. . . . . . . . 11
7.1.6 Robust Multiplatform Image/LiDAR Registration based on Geometric Primitives . . 12
7.2 Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
. . . . . . . 15
7.2.1 VoroMesh: Learning Watertight Surface Meshes with Voronoi Diagrams
7.2.2 Variational Shape Reconstruction via Quadric Error Metrics . . . . . . . . . . . . . . . 15
7.2.3 Progressive Shape Reconstruction from Raw 3D Point Clouds . . . . . . . . . . . . . . 15
7.2.4 A Survey and Benchmark of Automatic Surface Reconstruction from Point Clouds . 17
7.2.5 ConSLAM: Construction Data Set for SLAM . . . . . . . . . . . . . . . . . . . . . . . . 18
7.3 Approximation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
7.3.1 Feature-Preserving Offset Mesh Generation from Topology-Adapted Octrees . . . . . 20
7.3.2 Constructing Good Convex Polyhedral Decompositions from Polygons . . . . . . . . 20
7.3.3 Progressive Compression of Triangle Meshes . . . . . . . . . . . . . . . . . . . . . . . . 21

8 Bilateral contracts and grants with industry

22
8.1 Bilateral contracts with industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

9 Partnerships and cooperations

9.2 European initiatives

9.1 International research visitors

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. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
9.1.1 Visits of international scientists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
9.3 National initiatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
9.3.1 3IA Côte d’Azur . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
9.3.2 Inria challenge ROAD-AI - with CEREMA . . . . . . . . . . . . . . . . . . . . . . . . . . 30
9.3.3 PEPR NumPex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

9.2.1 H2020 projects

10 Dissemination

31
10.1 Promoting scientific activities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
10.1.1 Scientific events: organisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
10.1.2 Scientific events: selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
10.1.3 Journal
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
10.1.4 Leadership within the scientific community . . . . . . . . . . . . . . . . . . . . . . . . 32
10.1.5 Scientific expertise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
10.1.6 Research administration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
10.2 Teaching - Supervision - Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
10.2.1 Teaching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
10.2.2 Supervision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
10.2.3 Juries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

11 Scientific production

33
11.1 Major publications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
11.2 Publications of the year
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
11.3 Other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

Project TITANE

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Project-Team TITANE

Creation of the Project-Team: 2014 January 01

Keywords

Computer sciences and digital sciences

A5.3. – Image processing and analysis

A5.3.2. – Sparse modeling and image representation

A5.3.3. – Pattern recognition

A5.5.1. – Geometrical modeling

A8.3. – Geometry, Topology

A8.12. – Optimal transport

A9.2. – Machine learning

Other research topics and application domains

B2.5. – Handicap and personal assistances

B3.3. – Geosciences

B5.1. – Factory of the future

B5.6. – Robotic systems

B5.7. – 3D printing

B8.3. – Urbanism and urban planning

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Inria Annual Report 2023

1 Team members, visitors, external collaborators

Research Scientists

• Pierre Alliez [Team leader, INRIA, Senior Researcher, HDR]

• Florent Lafarge [INRIA, Senior Researcher, HDR]

Post-Doctoral Fellows

• Roberto Dyke [INRIA, Post-Doctoral Fellow]

• Raphael Sulzer [LUXCARTA and INRIA, Post-Doctoral Fellow, from Nov 2023]

• Raphael Sulzer [INRIA, Post-Doctoral Fellow, until Oct 2023]

PhD Students

• Moussa Bendjilali [ALTEIA, CIFRE, from Sep 2023]

• Marion Boyer [CNES and INRIA]

• Rao Fu [GEOMETRY FACTORY (EU ITN GRAPES)]

• Jacopo Iollo [INRIA (Défi ROAD-AI with CEREMA)]

• Nissim Maruani [INRIA (3IA Cote d’Azur)]

• Armand Zampieri [SAMP AI, CIFRE]

Technical Staff

• Abir Affane [INRIA and AI Verse (Inria Spinoff), Engineer, from May 2023]

• Jackson Campolattaro [Delf University, Engineer, from Feb 2023 until May 2023]

• Rahima Djahel [INRIA (EU BIM2TWIN), Engineer, from Mar 2023]

• Kacper Pluta [INRIA (EU BIM2TWIN), Engineer, until Aug 2023]

Interns and Apprentices

• Ashley Chen [INRIA, Intern, from Aug 2023 until Nov 2023, MIT]

• Gauthier Drumel [INRIA, Intern, from Apr 2023 until Jun 2023, Polytech’Nice]

• Kushagra Gupta [INRIA, Intern, from May 2023 until Jul 2023, IIT Delhi]

• Hanna Malet [INRIA, Intern, from Jun 2023 until Jul 2023, Université Dauphine-PSL]

• Vaibhav Seth [INRIA, Intern, from May 2023 until Jul 2023, IIT Delhi]

Administrative Assistant

• Florence Barbara [INRIA]

Visiting Scientists

• Gianmarco Cherchi [UNIV CAGLIARI, from Oct 2023 until Nov 2023]

• Thibault Lejemble [EPITA, from Jul 2023 until Sep 2023]

Project TITANE

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External Collaborators

• Andreas Fabri [GEOMETRY FACTORY]

• Johann Lussange [ENS PARIS, until May 2023]

• Sven Oesau [GEOMETRY FACTORY]

• Kacper Pluta [ESIEE, from Sep 2023]

• Laurent Rineau [GEOMETRY FACTORY]

• Mael Rouxel-Labbé [GEOMETRY FACTORY]

2 Overall objectives

2.1 General Presentation

Our overall objective is the computerized geometric modeling of complex scenes from physical meas-
urements. On the geometric modeling and processing pipeline, this objective corresponds to steps
required for conversion from physical to effective digital representations: analysis, reconstruction and
approximation. Another longer term objective is the synthesis of complex scenes. This objective is related
to analysis as we assume that the main sources of data are measurements, and synthesis is assumed to be
carried out from samples.

The related scientific challenges include i) being resilient to defect-laden data due to the uncertainty
in the measurement processes and imperfect algorithms along the pipeline, ii) being resilient to hetero-
geneous data, both in type and in scale, iii) dealing with massive data, and iv) recovering or preserving the
structure of complex scenes. We define the quality of a computerized representation by its i) geometric
accuracy, or faithfulness to the physical scene, ii) complexity, iii) structure accuracy and control, and iv)
amenability to effective processing and high level scene understanding.

3 Research program

3.1 Context

Geometric modeling and processing revolve around three main end goals: a computerized shape repres-
entation that can be visualized (creating a realistic or artistic depiction), simulated (anticipating the real)
or realized (manufacturing a conceptual or engineering design). Aside from the mere editing of geometry,
central research themes in geometric modeling involve conversions between physical (real), discrete (di-
gital), and mathematical (abstract) representations. Going from physical to digital is referred to as shape
acquisition and reconstruction; going from mathematical to discrete is referred to as shape approximation
and mesh generation; going from discrete to physical is referred to as shape rationalization.

Geometric modeling has become an indispensable component for computational and reverse engin-
eering. Simulations are now routinely performed on complex shapes issued not only from computer-
aided design but also from an increasing amount of available measurements. The scale of acquired data
is quickly growing: we no longer deal exclusively with individual shapes, but with entire scenes, possibly
at the scale of entire cities, with many objects defined as structured shapes. We are witnessing a rapid
evolution of the acquisition paradigms with an increasing variety of sensors and the development of
community data, as well as disseminated data.

In recent years, the evolution of acquisition technologies and methods has translated in an increasing
overlap of algorithms and data in the computer vision, image processing, and computer graphics com-
munities. Beyond the rapid increase of resolution through technological advances of sensors and methods
for mosaicing images, the line between laser scan data and photos is getting thinner. Combining, e.g.,
laser scanners with panoramic cameras leads to massive 3D point sets with color attributes. In addition,
it is now possible to generate dense point sets not just from laser scanners but also from photogrammetry
techniques when using a well-designed acquisition protocol. Depth cameras are getting increasingly

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Inria Annual Report 2023

common, and beyond retrieving depth information we can enrich the main acquisition systems with
additional hardware to measure geometric information about the sensor and improve data registration:
e.g., accelerometers or GPS for geographic location, and compasses or gyrometers for orientation. Finally,
complex scenes can be observed at different scales ranging from satellite to pedestrian through aerial
levels.

These evolutions allow practitioners to measure urban scenes at resolutions that were until now
possible only at the scale of individual shapes. The related scientific challenge is however more than just
dealing with massive data sets coming from increase of resolution, as complex scenes are composed
of multiple objects with structural relationships. The latter relate i) to the way the individual shapes
are grouped to form objects, object classes or hierarchies, ii) to geometry when dealing with similar-
ity, regularity, parallelism or symmetry, and iii) to domain-specific semantic considerations. Beyond
reconstruction and approximation, consolidation and synthesis of complex scenes require rich structural
relationships.

The problems arising from these evolutions suggest that the strengths of geometry and images may
be combined in the form of new methodological solutions such as photo-consistent reconstruction. In
addition, the process of measuring the geometry of sensors (through gyrometers and accelerometers) of-
ten requires both geometry process and image analysis for improved accuracy and robustness. Modeling
urban scenes from measurements illustrates this growing synergy, and it has become a central concern
for a variety of applications ranging from urban planning to simulation through rendering and special
effects.

3.2 Analysis

Complex scenes are usually composed of a large number of objects which may significantly differ in terms
of complexity, diversity, and density. These objects must be identified and their structural relationships
must be recovered in order to model the scenes with improved robustness, low complexity, variable levels
of details and ultimately, semantization (automated process of increasing degree of semantic content).
Object classification is an ill-posed task in which the objects composing a scene are detected and
recognized with respect to predefined classes, the objective going beyond scene segmentation. The high
variability in each class may explain the success of the stochastic approach which is able to model widely
variable classes. As it requires a priori knowledge this process is often domain-specific such as for urban
scenes where we wish to distinguish between instances as ground, vegetation and buildings. Additional
challenges arise when each class must be refined, such as roof super-structures for urban reconstruction.
Structure extraction consists in recovering structural relationships between objects or parts of object.
The structure may be related to adjacencies between objects, hierarchical decomposition, singularities or
canonical geometric relationships. It is crucial for effective geometric modeling through levels of details
or hierarchical multiresolution modeling. Ideally we wish to learn the structural rules that govern the
physical scene manufacturing. Understanding the main canonical geometric relationships between
object parts involves detecting regular structures and equivalences under certain transformations such as
parallelism, orthogonality and symmetry. Identifying structural and geometric repetitions or symmetries
is relevant for dealing with missing data during data consolidation.

Data consolidation is a problem of growing interest for practitioners, with the increase of heterogen-
eous and defect-laden data. To be exploitable, such defect-laden data must be consolidated by improving
the data sampling quality and by reinforcing the geometrical and structural relations sub-tending the
observed scenes. Enforcing canonical geometric relationships such as local coplanarity or orthogonality
is relevant for registration of heterogeneous or redundant data, as well as for improving the robustness of
the reconstruction process.

3.3 Approximation

Our objective is to explore the approximation of complex shapes and scenes with surface and volume
meshes, as well as on surface and domain tiling. A general way to state the shape approximation problem
is to say that we search for the shape discretization (possibly with several levels of detail) that realizes
the best complexity / distortion trade-off. Such a problem statement requires defining a discretization
model, an error metric to measure distortion as well as a way to measure complexity. The latter is most

Project TITANE

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commonly expressed in number of polygon primitives, but other measures closer to information theory
lead to measurements such as number of bits or minimum description length.

For surface meshes we intend to conceive methods which provide control and guarantees both over
the global approximation error and over the validity of the embedding. In addition, we seek for resilience
to heterogeneous data, and robustness to noise and outliers. This would allow repairing and simplifying
triangle soups with cracks, self-intersections and gaps. Another exploratory objective is to deal generically
with different error metrics such as the symmetric Hausdorff distance, or a Sobolev norm which mixes
errors in geometry and normals.

For surface and domain tiling the term meshing is substituted for tiling to stress the fact that tiles
may be not just simple elements, but can model complex smooth shapes such as bilinear quadrangles.
Quadrangle surface tiling is central for the so-called resurfacing problem in reverse engineering: the goal
is to tile an input raw surface geometry such that the union of the tiles approximates the input well and
such that each tile matches certain properties related to its shape or its size. In addition, we may require
parameterization domains with a simple structure. Our goal is to devise surface tiling algorithms that are
both reliable and resilient to defect-laden inputs, effective from the shape approximation point of view,
and with flexible control upon the structure of the tiling.

3.4 Reconstruction

Assuming a geometric dataset made out of points or slices, the process of shape reconstruction amounts
to recovering a surface or a solid that matches these samples. This problem is inherently ill-posed as
infinitely-many shapes may fit the data. One must thus regularize the problem and add priors such as
simplicity or smoothness of the inferred shape.

The concept of geometric simplicity has led to a number of interpolating techniques commonly based
upon the Delaunay triangulation. The concept of smoothness has led to a number of approximating
techniques that commonly compute an implicit function such that one of its isosurfaces approximates
the inferred surface. Reconstruction algorithms can also use an explicit set of prior shapes for inference
by assuming that the observed data can be described by these predefined prior shapes. One key lesson
learned in the shape problem is that there is probably not a single solution which can solve all cases, each
of them coming with its own distinctive features. In addition, some data sets such as point sets acquired
on urban scenes are very domain-specific and require a dedicated line of research.

In recent years the smooth, closed case (i.e., shapes without sharp features nor boundaries) has
received considerable attention. However, the state-of-the-art methods have several shortcomings:
in addition to being in general not robust to outliers and not sufficiently robust to noise, they often
require additional attributes as input, such as lines of sight or oriented normals. We wish to devise
shape reconstruction methods which are both geometrically and topologically accurate without requiring
additional attributes, while exhibiting resilience to defect-laden inputs. Resilience formally translates into
stability with respect to noise and outliers. Correctness of the reconstruction translates into convergence
in geometry and (stable parts of) topology of the reconstruction with respect to the inferred shape known
through measurements.

Moving from the smooth, closed case to the piecewise smooth case (possibly with boundaries) is
considerably harder as the ill-posedness of the problem applies to each sub-feature of the inferred shape.
Further, very few approaches tackle the combined issue of robustness (to sampling defects, noise and
outliers) and feature reconstruction.

4 Application domains

In addition to tackling enduring scientific challenges, our research on geometric modeling and processing
is motivated by applications to computational engineering, reverse engineering, robotics, digital mapping
and urban planning. The main outcome of our research will be algorithms with theoretical foundations.
Ultimately, we wish to contribute making geometry modeling and processing routine for practitioners
who deal with real-world data. Our contributions may also be used as a sound basis for future software
and technology developments.

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Inria Annual Report 2023

Our first ambition for technology transfer is to consolidate the components of our research exper-
iments in the form of new software components for the CGAL (Computational Geometry Algorithms
Library). Through CGAL, we wish to contribute to the “standard geometric toolbox”, so as to provide a
generic answer to application needs instead of fragmenting our contributions. We already cooperate with
the Inria spin-off company Geometry Factory, which commercializes CGAL, maintains it and provides
technical support.

Our second ambition is to increase the research momentum of companies through advising Cifre

Ph.D. theses and postdoctoral fellows on topics that match our research program.

5 Highlights of the year

• We are involved in strategic Inria partnerships: Inria challenge with CEREMA, collaboration with
CNES on remote sensing, and project with Naval Group on the factory of the future (in collaboration
with the Acentauri Inria project-team).

• We increased our collaborations with GeometryFactory with several research and development
projets relating to the CGAL library (reliable computation of offsets, feature-preserving alpha
wrapping, kinetic space partition and reconstruction, semantic classification of point sets, surface
reconstruction via clustering quadric error metrics), and the organization of the CGAL developer
meeting with 20 participants.

• We organized the third Inria-DFKI European Summer School on AI (IDESSAI 2023) in coordination

with 3IA Côte d’Azur, with around 100 participants.

• Pierre Alliez is chairing the Inria evaluation committee since September 2023.

5.1 Awards

The open source project CGAL (Computational Geometry Algorithms) received the Test of Time award at
the International Symposium on Computational Geometry (SoCG) 2023, in Dallas. CGAL is the fruit of
close to 30 years of development, with significant contributions from Inria researchers. It features the
latest innovations in computational geometry and geometry processing research.

6 New software, platforms, open data

6.1 New software

6.1.1 CGAL - NURBS meshing

Name: CGAL - Meshing NURBS surfaces via Delaunay refinement

Keywords: Meshing, NURBS

Scientific Description: NURBS is the dominant boundary representation (B-Rep) in CAD systems. The
meshing algorithms of NURBS models available for the industrial applications are based on the
meshing of individual NURBS surfaces. This process can be hampered by the inevitable trimming
defects in NURBS models, leading to non-watertight surface meshes and further failing to generate
volumetric meshes. In order to guarantee the generation of valid surface and volumetric meshes
of NURBS models even with the presence of trimming defects, NURBS models are meshed via
Delaunay refinement, based on the Delaunay oracle implemented in CGAL. In order to achieve the
Delaunay refinement, the trimmed regions of a NURBS model are covered with balls. Protection
balls are used to cover sharp features. The ball centres are taken as weighted points in the Delaunay
refinement, so that sharp features are preserved in the mesh. The ball sizes are determined with
local geometric features. Blending balls are used to cover other trimmed regions which do not need
to be preserved. Inside blending balls implicit surfaces are generated with Duchon’s interpolating
spline with a handful of sampling points. The intersection computation in the Delaunay refinement

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depends on the region where the intersection is computed. The general line/NURBS surface
intersection is computed for intersections away from trimmed regions, and the line/implicit
surface intersection is computed for intersections inside blending balls. The resulting mesh is
a watertight volumetric mesh, satisfying user-defined size and shape criteria for facets and cells.
Sharp features are preserved in the mesh adaptive to local features, and mesh elements cross
smooth surface boundaries.

Functional Description: Input: NURBS surface Output: isotropic tetrahedron mesh

Publication: hal-03276860

Contact: Pierre Alliez

Participants: Pierre Alliez, Laurent Busé, Xiao Xiao, Laurent Rineau

Partner: GeometryFactory

6.1.2 3D_Roof_Modeler

Name: 3D_Roof_Modeler

Keyword: Geometry Processing

Functional Description: This software takes as input (i) a set of 2D polygons representing the footprints
of a building, and (ii) additionnal knowledge on each edge of the polygons to specify how the roof
should connect to the facade wall, and provides as output a 3D model of the building in the form of
a watertight polygonal surface mesh in which facets of roof, facade and ground are identified.

Author: Florent Lafarge

Contact: Florent Lafarge

6.1.3 ASIP

Name: Approximation of Shape in Images by Polygons

Keywords: Shape approximation, Computer vision, Vectorization, Polygons

Functional Description: This software allows the extraction and vectorization of objects in images with
polygons. Departing from a polygonal partition that oversegments an image into convex cells, the
algorithm refines the geometry of the partition while labeling its cells by a semantic class. The
result is a set of polygons, each capturing an object in the image. The quality of a configuration is
measured by an energy that accounts for both the fidelity to input data and the complexity of the
output polygons. To efficiently explore the configuration space, splitting and merging operations
are performed in tandem on the cells of the polygonal partition. The exploration mechanism is
controlled by a priority queue that sorts the operations most likely to decrease the energy.

Publication: hal-02526028

Authors: Muxingzi Li, Florent Lafarge

Contact: Florent Lafarge

6.1.4 BSPslicer

Keywords: Computer vision, Geometric modeling

Functional Description: This software takes as input a set of planes detected from 3D data measure-
ments and computes a compact plane arrangement that decomposes the space into convex poly-
hedra. The planes are processed in a specific order that reduces the complexity of the decomposi-
tion and of the underlying Binary Space Partitioning (BSP) tree.

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Inria Annual Report 2023

Authors: Raphael Sulzer, Florent Lafarge

Contact: Florent Lafarge

6.1.5 GoCoPP

Name: Detection of planar primitives in point clouds by energy-based formulation

Keywords: Computer vision, Point cloud, Geometry Processing

Functional Description: This software detects planar primitives from unorganized 3D point clouds.
Departing from an initial configuration, the algorithm refines both the continuous plane parameters
and the discrete assignment of input points to them by seeking high fidelity, high simplicity and
high completeness. Our key contribution relies upon the design of an exploration mechanism
guided by a multi-objective energy function. The transitions within the large solution space are
handled by five geometric operators that create, remove and modify primitives.

Publication: hal-03621896

Authors: Mulin Yu, Florent Lafarge

Contact: Florent Lafarge

Partner: CSTB

6.1.6 Feature-Preserving Offset Mesh Generation from Topology-Adapted Octrees

Keyword: Offset surface

Functional Description: A reliable method to generate offset meshes from input triangle meshes or
triangle soups. Our method proceeds in two steps. The first step performs a Dual Contouring
method on the offset surface, operating on an adaptive octree that is refined in areas where the
offset topology is complex. Our approach substantially reduces memory consumption and runtime
compared to isosurfacing methods operating on uniform grids. The second step improves the
output Dual Contouring mesh with an offset-aware remeshing algorithm to reduce the normal
deviation between the mesh facets and the exact offset. This remeshing process reconstructs
concave sharp features and approximates smooth shapes in convex areas up to a user-defined
precision. We show the effectiveness and versatility of our method by applying it to a wide range
of input meshes. We benchmarked our method on the entire Thingi10k dataset: watertight, 2-
manifold offset meshes are obtained for 100% of the cases.

Authors: Pierre Alliez, Daniel Zint, Mael Rouxel-Labbé

Contact: Pierre Alliez

Partner: GeometryFactory

6.1.7 PlaneRegul

Keywords: Geometry Processing, Computer vision

Functional Description: This software takes as input a 3D point cloud that samples the surface of
physical objects and scenes, and fits a set of polygons on it while preserving geometric regularities
existing in between polygons such as parallelism, orthogonality or co-linearity.

Author: Florent Lafarge

Contact: Florent Lafarge

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7.1 Analysis

9

7.1.1 Sharp Feature Consolidation from Raw 3D Point Clouds via Displacement Learning

Participants:

Pierre Alliez, Florent Lafarge, Tong Zhao (former PhD student, now
at Dassault Systemes), Florent Lafarge (former PhD student, now at
Shanghai Artificial Intelligence Laboratory).

Detecting sharp features in raw point clouds is an essential step in designing efficient priors in several
3D Vision applications. This work presents a deep learning-based approach that learns to detect and
consolidate sharp feature points on raw 3D point clouds (See Figure 1). We devise a multi-task neural
network architecture that identifies points near sharp features and predicts displacement vectors toward
the local sharp features. The so-detected points are thus consolidated via relocation. Our approach is
robust against noise by utilizing a dynamic labeling oracle during the training phase. The approach is also
flexible and can be combined with several popular point-based network architectures. Our experiments
demonstrate that our approach outperforms the previous work in terms of detection accuracy measured
on the popular ABC dataset. We show the efficacy of the proposed approach by applying it to several 3D
Vision tasks. This work was published in the Computer Aided Geometric Design journal [14].

Figure 1: Sharp Feature Consolidation. The proposed architecture comprises four modules: point-to-
feature oracle (purple dash), point descriptor extractor (red dash), sharp feature detector (green dash) and
displacement predictor (blue dash). Point-to-feature oracle module is only used for training. It introduces
noise to the input, computes the ground truth classification labels and the ground truth displacement
vectors. A backbone is used to extract high dimensional representations f (size: n × D2) in the point
descriptor extractor module. MLP stands for multi-layer perceptron. Cla and Dis represent binary sharp
feature labels and displacement vectors, respectively. (cid:76) adds the corresponding predicted displacement
vectors to detected sharp feature points to make them lie closer to the ground truth sharp features. BC E
and M asked M SE are two losses calculated based on Cla, Dis and the ground truth generated by the
oracle.

7.1.2 BPNet: Bézier Primitive Segmentation on 3D Point Clouds

Participants: Rao Fu, Pierre Alliez, Cheng wen (University of Sydney), Qian Li (Mi-
metic Inria project-team), Xiao Xiao (former postdoctoral fellow, now
at Shanghai Jiao Tong University).

This work proposes BPNet, a novel end-to-end deep learning framework to learn Bézier primitive seg-
mentation on 3D point clouds. The existing works treat different primitive types separately, thus limiting
them to finite shape categories. To address this issue, we seek a generalized primitive segmentation on

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point clouds. Taking inspiration from Bézier decomposition on NURBS models, we transfer it to guide
point cloud segmentation casting off primitive types. A joint optimization framework is proposed to
learn Bézier primitive segmentation and geometric fitting simultaneously on a cascaded architecture
(see Figure 2). Specifically, we introduce a soft voting regularizer to improve primitive segmentation
and propose an auto-weight embedding module to cluster point features, making the network more
robust and generic. We also introduce a reconstruction module where we successfully process multiple
CAD models with different primitives simultaneously. We conducted extensive experiments on the syn-
thetic ABC dataset and real-scan datasets to validate and compare our approach with different baseline
methods. Experiments show superior performance over previous work in terms of segmentation, with a
substantially faster inference speed. This work was presented at the International Joint Conference on
Artificial Intelligence (IJCAI) [16].

Figure 2: BPNet architecture. The network takes a point cloud as input. It outputs pointwise features
followed by four modules to predict Bézier geometry and topology: (1) The decomposition module
decomposes point clouds into multiple patches. (2) The fitting module regresses uv parameters for each
point and control points for each Bézier patch. (3) The embedding module clusters pointwise features
assigned to the same patch. (4) The reconstruction module re-evaluates the input point clouds from the
above predictions.

7.1.3 KIBS: 3D Detection of Planar Roof Sections from a Single Satellite Image

Participants:

Johann Lussange, Florent Lafarge, Yuliya Tarabalka (Luxcarta),
Mulin Yu (former PhD student, now at Shanghai Artificial Intelligence
Laboratory).

Reconstructing urban areas in 3D from satellite raster images has been a long-standing problem for
both academical and industrial research. While automatic methods achieving this objective at a Level Of
Details (LOD) 1 are efficient today, producing LOD2 models is still a scientific challenge. In particular,
the quality and resolusion of satellite data is too low to infer accurately the planar roof sections in 3D by
traditional plane detection algorithms. Only inverse procedural modeling methods and skeletonization
algorithms can approximate LOD2 models that often strongly deviate from the real scenes. In this work,
we address this issue with KIBS (Keypoints Inference By Segmentation), a method that detects planar roof
sections in 3D from a single satellite image (see Figure 3). By exploiting recent deep learning architectures
with existing large-scale LOD2 databases produced by human operators, we achieve to both segment roof
sections in images and lift keypoints enclosing these sections in 3D to form 3D-polygons. The output
set of 3D-polygons can be used to reconstruct LOD2 models of buildings when combined to a plane
assembly method. We demonstrate the potential of KIBS by reconstructing different urban areas in a few
minutes, with a Jaccard index for the 2D segmentation of individual roof sections of approximatively 80%,

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and an altimetric error of the reconstructed LOD2 model inferior to 2 meters. A preprint version of this
work is available on arXiv [22], currently under submission.

Figure 3: 3D detection of planar roof sections. KIBS detects the planar roof sections of buildings in large-
scale satellite image under the form of 3D polygons. In these visual results, polygons are backprojected
onto the input image (blue shapes). Red points refer to the vertices of the polygons.

7.1.4 Line-segment detection in images with fitting tolerance control

Participants: Marion Boyer, Florent Lafarge, David Youssefi (CNES).

This work proposes a geometric reformulation of the popular line segment detection problem from
images. In this reformulation, a line segment detected in the image does not necessarily refer to line-like
structures of objects, but can also approximate the local geometry of organic shapes within a fitting
tolerance. Our approach simultaneously seeks high fidelity, completeness and regularity in line segment
configurations with an energy minimization framework. This approach brings precision and global
consistency in the landscape of existing algorithms (see Figure 4). We show its competitiveness against
different families of methods on various datasets, especially for producing regularized configurations of
line segments. Operating from an image gradient map, our method can be combined with recent deep
learning approaches for the fitting step or simply used as post-processing for refining a result. This work
is currently under submission.

7.1.5 Registration for Urban Modeling Based on Linear and Planar Features

Participants:

Rahima Djahel, Pascal Monasse (Ecole des Ponts ParisTech), Bruno Val-
let (IGN).

The production of a Building Information Model (BIM) from an existing asset is currently expensive
and needs automation of the registration of the different acquisition data, including the registration

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Figure 4: Line-segment detection in images. Fitting tolerance control allows us to accurately locate
line segments on line-like structures while approximating curved shapes by chains of line segments (d).
Existing methods, ie incremental mechanism (a), end-to-end neural approach (b) and hybrid model (c),
sparsely describes freeform parts of the boat and overly detects on regular structures. Combined with a
vectorization method that assembles line-segments into intersection-free polygons, our algorithm can be
used to polygonize the silhouette of boat with high accuracy (e).

of indoor and outdoor data. This kind of registration is considered a challenging problem, especially
when both data sets are acquired separately and use different types of sensors (see Figure 5). Besides,
comparing a BIM to as-built data is an important factor to perform building progress monitoring and
quality control. To carry out this comparison, the data sets must be registered. In order to solve both
registration problems, we introduce two efficient algorithms. The first offers a potential solution for
indoor/outdoor registration based on heterogeneous features (openings and planes). The second is
based on linear features and proposes a potential solution for LiDAR data/BIM model registration. The
common point between the approaches consists in the definition of a global robust distance between
two segment sets and the minimization of this distance based on the RANSAC paradigm, finding the rigid
geometric transformation that is the most consistent with all the information in the data sets. This work
was presented at the European Workshop on Visual Information Processing [19].

7.1.6 Robust Multiplatform Image/LiDAR Registration based on Geometric Primitives

Participants:

Rahima Djahel, Pascal Monasse (Ecole des Ponts ParisTech), Bruno Val-
let (IGN).

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Figure 5: Heterogeneous data registration for urban modeling: (a) indoor /outdoor data registration to
simplify the production of a Building Information Model (BIM) from an existing asset. (b) BIM model
/LiDAR data registration to simplify the performance of building progress monitoring and Quality control.

Many applications benefit from data acquisition from various platforms and with different modalities.
A global approach can take advantage of such heterogeneous data sources by combining them to take
advantage of their semantic and geometric complementary. The challenge tackled in this work is to
register such data that can be heterogeneous in both platform and modality. We propose a generic
primitive based registration algorithm that can be adapted to tackle specific heterogeneous registration
problems. The type of primitives and the extraction method is chosen according to the properties of the
data, the environment and the acquisition platforms. The proposed generic registration relies on the
definition of a global energy between the primitives extracted from the data-sets to register, and on a
robust method to optimize this energy based on the RANSAC paradigm. The obtained results show the
ability of our algorithms to deal with the registration of heterogeneous data and to solve challenging prob-
lems including indoor/outdoor registration, image/LiDAR registration and aerial/terrestrial registration.
See Figure 6. This work is being prepared for submission.

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Figure 6: Overview of the registration methods studied in this work and their possible applications.

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7.2 Reconstruction

7.2.1 VoroMesh: Learning Watertight Surface Meshes with Voronoi Diagrams

Participants: Nissim Maruani, Pierre Alliez, Mathieu Desbrun (Geomerix In-
ria project team), Roman Klokov (Ecole Polytechnique), Maks Ovs-
janikov (Ecole Polytechnique).

In stark contrast to the case of images, finding a concise, learnable discrete representation of 3D
surfaces remains a challenge. In particular, while polygon meshes are arguably the most common
surface representation used in geometry processing, their irregular and combinatorial structure often
make them unsuitable for learning-based applications. In this work, we present VoroMesh, a novel and
differentiable Voronoi-based representation of watertight 3D shape surfaces. From a set of 3D points
(called generators) and their associated occupancy, we define our boundary representation through the
Voronoi diagram of the generators as the subset of Voronoi faces whose two associated (equidistant)
generators are of opposite occupancy: the resulting polygon mesh forms a watertight approximation of
the target shape’s boundary (see Figure 7). To learn the position of the generators, we propose a novel
loss function, dubbed VoroLoss, that minimizes the distance from ground truth surface samples to the
closest faces of the Voronoi diagram which does not require an explicit construction of the entire Voronoi
diagram. A direct optimization of the VoroLoss to obtain generators on the Thingi32 dataset demonstrates
the geometric efficiency of our representation compared to axiomatic meshing algorithms and recent
learning-based mesh representations. We further use VoroMesh in a learning-based mesh prediction
task from input SDF grids on the ABC dataset, and show comparable performance to state-of-the-art
methods while guaranteeing closed output surfaces free of self-intersections. This work was presented at
the International Conference on Computer Vision [17].

7.2.2 Variational Shape Reconstruction via Quadric Error Metrics

Participants:

Tong Zhao, Pierre Alliez, Laurent Busé (AROMATH Inria projet-
team), David Cohen-Steiner (DATASHAPE Inria project-team),
Tamy Boubekeur (Adobe Research), Jean-Marc Thiery (Adobe Re-
search).

Inspired by the strengths of quadric error metrics initially designed for mesh decimation, we propose
a concise mesh reconstruction approach for 3D point clouds. Our approach proceeds by clustering the
input points enriched with quadric error metrics, where the generator of each cluster is the optimal
3D point for the sum of its quadric error metrics. This approach favors the placement of generators on
sharp features, and tends to equidistribute the error among clusters (see Figure 8). We reconstruct the
output surface mesh from the adjacency between clusters and a constrained binary solver. We combine
our clustering process with an adaptive refinement driven by the error. Compared to prior art, our
method avoids dense reconstruction prior to simplification and produces immediately an optimized
mesh. This work was presented at the ACM SIGGRAPH conference [18]. We are currently working on
a new component for the CGAL library, with support from the Google summer of code program and
cooperation with GeometryFactory.

7.2.3 Progressive Shape Reconstruction from Raw 3D Point Clouds

Participants:

Tong Zhao.

This Ph.D. thesis funded by 3IA Côte d’Azur was co-advised by Pierre Alliez and Laurent Busé (Aromath
Inria project-team).

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Inria Annual Report 2023

Figure 7: VoroMesh at a glance. To fit our representation to (a) a target shape (top: curve; bottom: surface),
(b) the positions of generators around the shape boundary are optimized based on our VoroLoss, before
a binary occupancy is assigned to each generator; (c) the VoroMesh is then extracted as a subset of the
Voronoi diagram of the generators containing only the Voronoi faces between pairs of generators of
opposite occupancy.

With the enthusiasm for digital twins in the fourth industrial revolution, surface reconstruction
from raw 3D point clouds is increasingly needed while facing multifaceted challenges. Advanced data
acquisition devices makes it possible to obtain 3D point clouds with multi-scale features. Users expect
controllable surface reconstruction approaches with meaningful criteria such as preservation of sharp
features or satisfactory accuracy-complexity tradeoffs.This thesis addresses these issues by contributing
several approaches to the problem of surface reconstruction from defect-laden point clouds. We first
propose the notion of progressive discrete domain for global implicit reconstruction approaches that
refines and optimizes a discrete 3D domain in accordance to both input and output, and to user-defined
criteria. Based on such a domain discretization, we devise a progressive primitive-aware surface recon-
struction approach with capacity to refine the implicit function and its representation, in which the most
ill-posed parts of the reconstruction problem are postponed to later stages of the reconstruction, and
where the fine geometric details are resolved after discovering the topology. Secondly, we contribute a
deep learning-based approach that learns to detect and consolidate sharp feature points on raw 3D point
clouds, whose results can be taken as an additional input to consolidate sharp features for the previous
reconstruction approach. Finally, we contribute a coarse-to-fine piecewise smooth surface reconstruc-
tion approach that proceeds by clustering quadric error metrics. This approach outputs a simplified
reconstructed surface mesh, whose vertices are located on sharp features and whose connectivity is

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Figure 8: Variational Shape Reconstruction. The clustering of the points is randomly initialized, then
alternates partitioning and generator updating. Some generators relocate to sharp features after one
iteration. New generators are then added. The clustering converges after five iterations. A set of candidate
edges is derived from the adjacency between clusters, and candidate facets (red) are generated. The
output mesh is reconstructed via a constrained binary solver that selects a subset of the red facets.

solved by a binary problem solver. In summary, this thesis seeks for effective surface reconstruction from
a global and progressive perspective. By combining multiple priors and designing meaningful criteria,
the contributed approaches can deal with various defects and multi-scale features [20].

7.2.4 A Survey and Benchmark of Automatic Surface Reconstruction from Point Clouds

Participants:

Raphael Sulzer, Loic Landrieu (Ecole des Ponts Paristech), Renaud Mar-
let (Valeo.ai), Bruno Vallet (IGN).

We survey and benchmark traditional and novel learning-based algorithms that address the problem
of surface reconstruction from point clouds. Surface reconstruction from point clouds is particularly
challenging when applied to real-world acquisitions, due to noise, outliers, non-uniform sampling and
missing data. Traditionally, different handcrafted priors of the input points or the output surface have
been proposed to make the problem more tractable. However, hyperparameter tuning for adjusting
priors to different acquisition defects can be a tedious task. To this end, the deep learning community
has recently addressed the surface reconstruction problem. In contrast to traditional approaches, deep
surface reconstruction methods can learn priors directly from a training set of point clouds and corres-
ponding true surfaces. In our survey, we detail how different handcrafted and learned priors affect the
robustness of methods to defect-laden input and their capability to generate geometric and topologically

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Figure 9: Benchmark of Automatic Surface Reconstruction from Point Clouds. Learning- and
optimization-based reconstructions of a real-world MVS point cloud. The learning methods (b-j) were
trained on synthetic MVS scans from ShapeNet. Optimization-based methods are optimized per scan
using standard settings. The two traditional methods SPSR (k) and Labatut et al. (i) reconstruct the
visually best surfaces.

accurate reconstructions. In our benchmark, we evaluate the reconstructions of several traditional and
learning-based methods on the same grounds (see Figure 9). We show that learning-based methods
can generalize to unseen shape categories, but their training and test sets must share the same point
cloud characteristics. We also provide the code and data to compete in our benchmark and to further
stimulate the development of learning-based surface reconstruction (link). A preprint version of this
work is available on arXiv [21], currently under revision at TPAMI.

7.2.5 ConSLAM: Construction Data Set for SLAM

Participants: Kacper Pluta, Pierre Alliez, Maciej Trzeciak (University of Cambridge),
Yasmin Fathy (University of Cambridge), Ioannis Brilakis (University
of Cambridge), Lucio Alcalde (Laing O’Rourke), Stanley Chee (Laing
O’Rourke), Antony Bromley (Laing O’Rourke).

This work presents a dataset collected periodically on a construction site. The dataset aims to evaluate
the performance of SLAM algorithms used by mobile scanners or autonomous robots. It includes ground-
truth scans of a construction site collected using a terrestrial laser scanner along with five sequences
of spatially registered and time-synchronized images, LiDAR scans and inertial data coming from our
prototypical hand-held scanner (see Figure 10). We also recover the ground-truth trajectory of the
mobile scanner by registering the sequential LiDAR scans to the ground-truth scans and show how to
use a popular software package to measure the accuracy of SLAM algorithms against our trajectory
automatically. To the best of our knowledge, this is the first publicly accessible dataset consisting of
periodically collected sequential data on a construction site. This work was published in the Journal of
Computing in Civil Engineering [12].

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Figure 10: Close-up visualization of ground-truth datasets. The images include also the LiDAR positions
depicted by red spheres. The positions have been connected to provide approximated paths/trajectories.

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7.3 Approximation

7.3.1 Feature-Preserving Offset Mesh Generation from Topology-Adapted Octrees

Participants: Nissim Maruani, Pierre Alliez, Daniel Zint (New York University),

Mael Rouxel-Labbé (GeometryFactory).

Figure 11: Feature-Preserving Offset Mesh Generation. (a) The input consists of a mesh and two main
user-defined parameters: offset radius and maximum normal deviation; (b,d) The topology of the initial
offset mesh is computed with Dual Contouring on a topology-adapted octree; (c,e) A high-quality offset
mesh is obtained through remeshing.

We introduce a reliable method to generate offset meshes from input triangle meshes or triangle
soups. Our method proceeds in two steps. The first step performs a Dual Contouring method on the offset
surface, operating on an adaptive octree that is refined in areas where the offset topology is complex. Our
approach substantially reduces memory consumption and runtime compared to isosurfacing methods
operating on uniform grids. The second step improves the output Dual Contouring mesh with an offset-
aware remeshing algorithm to reduce the normal deviation between the mesh facets and the exact offset.
This remeshing process reconstructs concave sharp features and approximates smooth shapes in convex
areas up to a user-defined precision (see Figure 11). We show the effectiveness and versatility of our
method by applying it to a wide range of input meshes. We also benchmark our method on the Thingi10k
dataset: watertight and topologically 2-manifold offset meshes are obtained for 100% of the cases. This
work was presented at the Eurographics Symposium on Geometry Processing [15].

7.3.2 Constructing Good Convex Polyhedral Decompositions from Polygons

Participants:

Raphael Sulzer, Florent Lafarge.

Decomposing a 3D domain into convex polyhedra from a set of polygons is a long standing problem
in piecewise planar geometry. Existing works target either quality of the output decomposition or
performance of its construction. We present an algorithm that addresses both. Departing from the
algorithm of Murali and Funkhouser that iteratively slices polyhedra that contain input polygons only,
we propose a series of technical improvements that allow us to produce more compact and meaningful
partitions, in less time, than existing methods. In particular, one of our key ideas is to give priority to
slicing operations that minimize the number of intersection computations necessary to construct the

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decomposition. We also propose a fast analysis of relative spatial positioning of polygons via point
sampling and a cell simplification process based on a Binary Space Partitioning tree analysis. We evaluate
and compare our approach with the existing polyhedral decomposition methods on a collection of input
polygons captured from real-world data measurements. We also show its efficiency on two applicative
tasks: compact mesh reconstruction and convex decomposition of 3D meshes (see Figure 12). This work
is currently under submission.

Figure 12: Convex Polyhedral Decompositions from Polygons. Our plane arrangement built from polygons
detected from the input point cloud allows the reconstruction of buildings with many geometric details
at the scale of a city. The 4,000 non-intersecting convexes that finely describe this urban landscape can
be simplified by aggregating the convexes and authorizing their overlap. A simplification with 1,000
convexes typically removes facade and roof details. At 500 convexes, only complex buildings are still
described by more than one convex cell. Reducing to 200 convexes allows us to represent each building
block or skyscraper by a single convex cell.

7.3.3 Progressive Compression of Triangle Meshes

Participants:

Pierre Alliez, Lucas Dubouchet (Former Inria research engineer), Vin-
cent Vidal (LIRIS Laboratory, Lyon), Guillaume Lavoué (LIRIS Laborat-
ory, Lyon).

This paper details the first publicly available implementation of the progressive mesh compression
algorithm described in the paper entitled "Compressed Progressive Meshes" (R. Pajarola and J. Rossignac,
IEEE Transactions on Visualization and Computer Graphics, 6 (2000)). Our implementation is generic,
modular, and includes several improvements in the stopping criteria and final encoding. Given an input
2-manifold triangle mesh, an iterative simplification is performed, involving batches of edge collapse
operations guided by an error metric. During this compression step, all the information necessary for
the reconstruction (at the decompression step) is recorded and compressed using several key features:
geometric quantization, prediction, and spanning tree encoding. Our implementation enabled to carry
out an experimental comparison of several settings for the key parameters of the algorithm: the local
error metric, the position type of the resulting vertex (after collapse), and the geometric predictor. The
proposed implementation is publicly available through the MEPP2 platform. The algorithm can be used
either as a command-line executable or integrated into the MEPP2 GUI. The source code is written in
C++ and is accessible on the IPOL web page of this article, as well as on the GitHub page of MEPP2
(MEPP-team/MEPP2 project 2). This work was published in the Image Processing On Line (IPOL) journal
[13].

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Inria Annual Report 2023

8 Bilateral contracts and grants with industry

8.1 Bilateral contracts with industry

AlteIA - Cifre thesis

Participants:

Pierre Alliez, Moussa Bendjilali.

This Cifre Ph.D. thesis project, entitled “From 3D Point Clouds to Cognitive 3D Models”, started in

September 2023, for a total duration of 3 years.

Context. Digital twinning of large-scale scenes and industrial sites requires acquiring the geometry
and appearance of terrains, large-scale structures and objects. In this thesis, we assume that the acquisi-
tion is performed by an aerial laser scanner (LiDAR) attached to a helicopter. The generated 3D point
clouds record the point coordinates, intensities of reflectivity, and possibly other attributes such as colors.
These raw measurement data can be massive, in the order of 70 points per meter square, summing up
to several hundred million points for scenes extending over kilometers. Analyzing large-scale scenes
requires (1) enriching these data with an abstraction layer, (2) understanding the structure and semantic
of the scenes, (3) producing so-called cognitive 3D models that are searchable, and (4) detecting the
changes in the scenes over time.

Figure 13: Large-scale scene of an electric infrastructure. The 3D point clouds are obtained with a
helicopter equipped with a LiDAR sensor. Left: point cloud colored by the elevation; Right: dense
semantic segmentation prediction (of a deep learning model) with seven classes of interest: Man made
object, ground, vegetation, building, noise, powerline, pole.

We want to explore novel algorithms for addressing the aforementioned requirements. The input is
assumed to be a LiDAR 3D point cloud recording the point coordinates and surface reflectivity intensity
of a point set sampled over a physical scene. The objectives are multifaceted:

• Semantic segmentation. We wish to retrieve the semantic of the scene as well as the location, pose
and semantic classes of objects of interest. Ideally, we seek for dense semantic segmentation, where
the class of each sample point is inferred in order to yield a complete description of the physical
scene.

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• Instance segmentation. We wish to distinguish each instance of a semantic class so that all objects

of a scene can be enumerated.

• Abstraction. We wish to detect canonical primitives (planes, cylinders, spheres, etc.) that approxim-
ate (abstract) the objects of the physical scenes, and understand the relationships between them
(e.g., adjacency, containment, parallelism, orthogonality, coplanarity, concentricity). Abstraction
goes beyond the notion of geometric simplification, with the capability to forget the exquisite
details present in dense 3D point clouds.

• Object detection. Knowing a library of CAD models, we wish to detect and enumerate all occur-
rences of an object of interest used as a query. We assume some known information either on the
primitive shape and its parametric description, or on the shape of the query.

• Change detection. Series of LiDAR data acquired over time are required for monitoring changes of
industrial sites. The objective is to detect and characterize changes in the physical scene, with or
without any explicit taxonomy.

• Levels of details. The above tasks should be tackled with levels of details (LODs), both for the
geometry and semantic. LODs will relate to geometric tolerance errors and to a hierarchy of
semantic ontologies.

CNES - Airbus

Participants: Marion Boyer, Florent Lafarge.

The goal of this collaboration is to design an automatic pipeline for modeling in 3D urban scenes
under a CityGML LOD2 formalism using the new generation of stereoscopic satellite images, in particular
the 30cm resolution images offered by Pléiades Néo satellites (see Figure 14). Traditional methods usually
start with a semantic classification step followed by a 3D reconstruction of objects composing the urban
scene. Too often in traditional approaches, inaccuracies and errors from the classification phase are
propagated and amplified throughout the reconstruction process without any possible subsequent
correction. In contrast, our main idea consists in extracting semantic information of the urban scene and
in reconstructing the geometry of objects simultaneously. This project started in October 2022, for a total
duration of 3 years.

Figure 14: Samples of Pléiades Néo satellite images with Ground Truth building wireframes (yellow lines).
The goal of this collaboration is to automatically extract building wireframes and lift them in 3D to form
3D models under a CityGML LOD2 formalism.

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Luxcarta

Participants:

Raphael Sulzer, Florent Lafarge.

Inria Annual Report 2023

The main goal of this collaboration with Luxcarta is to develop new algorithms that improve the
geometry and topology of 3D models of buildings produced by the company. The algorithms should
detect and enforce (i) geometric regularities in 3D models, such as parallelism of roof edges or connection
of polygonal facets at exactly one vertex, and (ii) simplify models, e.g. by removing undesired facets or
incoherent roof typologies. These two objectives will have to be fulfilled under the constraint that the
geometric accuracy of the solution must remain close to the one of the input models. Assuming the
input 3D models are valid watertight polygon surface meshes, we will investigate metrics to quantify the
geometric regularities and the simplicity of the input models and will propose a formulation that allows
both continuous and discrete modifications. Continuous modifications will aim to better align vertices
and edges, in particular to reinforce geometric regularities. Discrete modifications will handle changes
in roof topology with, for instance, removal or creation of roof sections or new adjacencies between
them. The output model will have to be valid 2-manifold watertight polygon surface meshes. We will also
investigate optimization mechanisms to explore the large solution space of the problem efficiently. This
project started in November 2023, for a total duration of 1 year.

Naval group

Participants:

Pierre Alliez.

This project is in collaboration with the Acentauri project-team (a research engineer is co-advised
since November 2023). The context is that of the factory of the future for Naval Group, for submarines and
surface ships. As input, we have a digital model (e.g., a frigate), the equipment assembly schedule and
measurement data (images or Lidar). We wish to monitor the assembly site to compare the "as-designed"
with the "as-built" model. The main problem is a need for coordination on the construction sites for
decision making and planning optimization. We wish to enable following the progress of a real project
and to check its conformity with the help of a digital twin. Currently, since Naval Group needs to verify on
site, rounds of visits are required to validate the progress as well as the equipment. These rounds are time
consuming and not to mention the constraints of the site, such as the temporary absence of electricity or
the numerous temporary assembly and safety equipment. The objective of the project is to automate the
monitoring, with sensor intelligence to validate the work. Fixed sensor systems (e.g. cameras or Lidar) or
mobile (e.g. drones) sensors will be used, with the addition of smartphones/tablets carried by operators
on site.

Samp AI - Cifre thesis

Participants:

Armand Zampieri, Pierre Alliez.

This project (Cifre Ph.D. thesis) investigates algorithms for progressive, random access compression of
3D point clouds for digital twinning of industrial sites (see figure 15). The context is as follows. Laser scans
of industrial facilities contain massive amounts of 3D points which are very reliable visual representations
of these facilities. These points represent the positions, color attributes and other metadata about
object surfaces. Samp turns these 3D points into intelligent digital twins that can be searched and
interactively visualized on heterogeneous clients, via streamed services on the cloud. The 3D point clouds
are first structured and enriched with additional attributes such as semantic labels or hierarchical group
identifiers. Efficient streaming motivates progressive compression of these 3D point clouds, while local
search or visualization queries call for random accessible capabilities. Given an enriched 3D point cloud

Project TITANE

25

as input, the objective is to generate as output a compressed version with the following properties: (1)
Random accessible compressed (RAC) file format that provides random access (not just sequential access)
to the decompressed content, so that each compressed chunk can be decompressed independently,
depending on the area in focus on the client side. (2) Progressive compression: the enriched point cloud
is converted into a stream of refinements, with an optimized rate-distortion tradeoff allowing for early
access to a faithful version of the content during streaming. (3) Structure-preserving: all structural and
semantic information are preserved during compression, allowing for structure-aware queries on the
client. (4) Lossless at the end: allows the original data to be perfectly reconstructed from the compressed
data, when streaming is completed.

Figure 15: Digital twin of an industrial site. Image courtesy Samp AI.

Urbassist

Participants:

Florent Lafarge.

This project aims to develop and mature a roof modeler software for the company Urbassist. Given
a polygonal footprint of a building and some information related to (i) the height of the facade, (ii) the
slope of the roof, and (iii) the type of connexion between the facade walls and the planar roof sections,
we implemented a skeletonization algorithm that reconstructs the building in 3D under the form of a
polygonal surface mesh. The sofware has been tested and matured by using random footprints from the
French cadastral map database. This project started in March 2021, for a total duration of 2 years.

AI Verse

Participants:

Pierre Alliez, Abir Affane.

In collaboration with Guillaume Charpiat (Tau Inria project-team, Saclay) and AI Verse (Inria spin-off ).

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Inria Annual Report 2023

We are currently pursuing our collaboration with AI Verse on statistics for improving sampling quality,
applied to the parameters of a generative model for 3D scenes. A postdoctoral fellow (Abir Affane) funded
for three years has started this year. The research topic is described next. The AI Verse technology
is devised to create infinitely random and semantically consistent 3D scenes. This creation is fast,
consuming less than 4 seconds per labeled image. From these 3D scenes, the system is able to build
quality synthetic images that come with rich labels that are unbiased unlike manually annotated labels
(Figure 16). As for real data, no metric exists to evaluate the performance of the synthetic datasets to train
a neural network. We thus tend to favor the photorealism of the images but such a criterion is far from
being the best. The current technology provides a means to control a rich list of additional parameters
(quality of lighting, trajectory and intrinsic parameters of the virtual camera, selection and placement
of assets, degrees of occlusion of the objects, choice of materials, etc). Since the generation engine can
modify all of these parameters at will to generate many samples, we will explore optimization methods
for improving the sampling quality. Most likely, a set of samples generated randomly by the generative
model does not cover well the whole space of interesting situations, because of unsuited sampling laws
or of sampling realization issues in high dimensions. The main question is how to improve the quality of
this generated dataset, that one would like to be close somehow to the given target dataset (consisting of
examples of images that one would like to generate). For this, statistical analyses of these two datasets
and of their differences are required, in order to spot possible issues such as strongly under-represented
areas of the target domain. Then, sampling laws can be adjusted accordingly, possibly by optimizing their
hyper-parameters, if any.

Figure 16: Examples of beds generated by AI Verse technology.

9 Partnerships and cooperations

Participants:

Pierre Alliez, Florent Lafarge.

9.1 International research visitors

9.1.1 Visits of international scientists

Other international visits to the team

Project TITANE

Gianmarco Cherchi

Status: Researcher

Institution of origin: University of Cagliari

Country: Italy

Dates: From October 2023 until November 2023

27

Context of the visit: Collaboration with the team on low-complexity mesh reconstruction from 3D point

clouds

Mobility program/type of mobility: Research stay

Thibault Lejemble

Status: Researcher

Institution of origin: Epita

Country: France

Dates: From July 2023 until September 2023

Context of the visit: Collaboration with the team on semantic analysis of 3D point clouds

Mobility program/type of mobility: Research stay

9.2 European initiatives

9.2.1 H2020 projects

BIM2TWIN

Participants:

Pierre Alliez, Kacper Pluta, Rahima Djahel.

BIM2TWIN project on cordis.europa.eu

Title: BIM2TWIN: Optimal Construction Management & Production Control

Duration: From November 1, 2020 to April 30, 2024

Partners:

• INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUE (INRIA),

France

• SPADA CONSTRUCTION (SPADA CONSTRUCTION), France

• AARHUS UNIVERSITET (AU), Denmark

• FIRA GROUP OY, Finland

• INTSITE LTD (INTSITE), Israel

• THE CHANCELLOR MASTERS AND SCHOLARS OF THE UNIVERSITY OF CAMBRIDGE,

United Kingdom

• ORANGE SA (Orange), France

• UNISMART - FONDAZIONE UNIVERSITA DEGLI STUDI DI PADOVA (UNISMART), Italy

• FUNDACION TECNALIA RESEARCH & INNOVATION (TECNALIA), Spain

• TECHNISCHE UNIVERSITAET MUENCHEN (TUM), Germany

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Inria Annual Report 2023

• IDP INGENIERIA Y ARQUITECTURA IBERIA SL (IDP), Spain

• SITEDRIVE OY, Finland

• UNIVERSITA DEGLI STUDI DI PADOVA (UNIPD), Italy

• FIRA OY, Finland

• RUHR-UNIVERSITAET BOCHUM (RUHR-UNIVERSITAET BOCHUM), Germany

• CENTRE SCIENTIFIQUE ET TECHNIQUE DU BATIMENT (CSTB), France

• DANMARKS TEKNISKE UNIVERSITET (TECHNICAL UNIVERSITY OF DENMARK DTU),

Denmark

• TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY, Israel

• UNIVERSITA POLITECNICA DELLE MARCHE (UNIVPM), Italy

• ACCIONA CONSTRUCCION SA, Spain

• SIEMENS AKTIENGESELLSCHAFT, Germany

Inria contact: Pierre Alliez

Coordinator: Bruno Fies, CSTB

Summary: BIM2TWIN aims to build a Digital Building Twin (DBT) platform for construction man-
agement that implements lean principles to reduce operational waste of all kinds, shortening
schedules, reducing costs, enhancing quality and safety and reducing carbon footprint. BIM2TWIN
proposes a comprehensive, holistic approach. It consists of a (DBT) platform that provides full
situational awareness and an extensible set of construction management applications. It supports
a closed loop Plan-Do-Check-Act mode of construction. Its key features are:

1> Grounded conceptual analysis of data, information and knowledge in the context of DBTs, which
underpins a robust system architecture

2> A common platform for data acquisition and complex event processing to interpret multiple
monitored data streams from construction site and supply chain to establish real-time project
status in a Project Status Model (PSM)

3> Exposure of the PSM to a suite of construction management applications through an easily ac-
cessible application programming interface (API) and directly to users through a visual information
dashboard

4> Applications include monitoring of schedule, quantities & budget, quality, safety, and environ-
mental impact.

5> PSM representation based on property graph semanticaly linked to the Building Information
Model (BIM) and all project management data. The property graph enables flexible, scalable
storage of raw monitoring data in different formats, as well as storage of interpreted information. It
enables smooth transition from construction to operation.

BIM2TWIN is a broad, multidisciplinary consortium with hand-picked partners who together
provide an optimal combination of knowledge, expertise and experience in a variety of monitoring
technologies, artificial intelligence, computer vision, information schema and graph databases,
construction management, equipment automation and occupational safety. The DBT platform will
be experimented on 3 demo sites (Spain, France, Finland).

GRAPES

Participants:

Pierre Alliez, Rao Fu.

Title: GRAPES: Learning, Processing, and Optimising Shapes

Duration: From December 1, 2019 to May 31, 2024

Project TITANE

Partners:

29

• NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUE (INRIA), France

• UNIVERSITA DEGLI STUDI DI ROMA TOR VERGATA (UNITOV), Italy

• RHEINISCH-WESTFAELISCHE TECHNISCHE HOCHSCHULE AACHEN (RWTH AACHEN),

Germany

• ATHINA-EREVNITIKO KENTRO KAINOTOMIAS STIS TECHNOLOGIES TIS PLIROFORIAS,
TON EPIKOINONION KAI TIS GNOSIS (ATHENA - RESEARCH AND INNOVATION CENTER),
Greece

• UNIVERSITAT LINZ (JOHANNES KEPLER UNIVERSITAT LINZ UNIVERSITY OF LINZ JO-

HANNES KEPLER UNIVERSITY OF LINZ JKU), Austria

• SINTEF AS (SINTEF), Norway

• VILNIAUS UNIVERSITETAS (Vilniaus universitetas), Lithuania

• UNIVERSITA DELLA SVIZZERA ITALIANA (USI), Switzerland

• UNIVERSITAT DE BARCELONA (UB), Spain

• GEOMETRY FACTORY SARL, France

• UNIVERSITY OF STRATHCLYDE, United Kingdom

Inria contact: Laurent Busé

Coordinator: Ioannis Emiris (ATHENA, Greece)

Summary: GRAPES aims at considerably advancing the state of the art in Mathematics, Computer-Aided
Design, and Machine Learning in order to promote game changing approaches for generating,
optimizing, and learning 3D shapes, along with a multisectoral training for young researchers.
Recent advances in the above domains have solved numerous tasks concerning multimedia and 2D
data. However, automation of 3D geometry processing and analysis lags severely behind, despite
their importance in science, technology and everyday life, and the well-understood underlying
mathematical principles. The CAD industry, although well established for more than 20 years,
urgently requires advanced methods and tools for addressing new challenges.

The scientific goal of GRAPES is to bridge this gap based on a multidisciplinary consortium com-
posed of leaders in their respective fields. Top-notch research is also instrumental in forming
the new generation of European scientists and engineers. Their disciplines span the spectrum
from Computational Mathematics, Numerical Analysis, and Algorithm Design, up to Geometric
Modeling, Shape Optimization, and Deep Learning. This allows the 15 PhD candidates to follow
either a theoretical or an applied track and to gain knowledge from both research and innovation
through a nexus of intersectoral secondments and Network-wide workshops.

Horizontally, our results lead to open-source, prototype implementations, software integrated into
commercial libraries as well as open benchmark datasets. These are indispensable for dissemin-
ation and training but also to promote innovation and technology transfer. Innovation relies on
the active participation of SMEs, either as a beneficiary hosting an ESR or as associate partners
hosting secondments. Concrete applications include simulation and fabrication, hydrodynamics
and marine design, manufacturing and 3D printing, retrieval and mining, reconstruction and
visualization, urban planning and autonomous driving.

9.3 National initiatives

9.3.1 3IA Côte d’Azur

Pierre Alliez holds a senior chair from 3IA Côte d’Azur (Interdisciplinary Institute for Artificial Intelligence).
The topic of his chair is “3D modeling of large-scale environments for the smart territory”. In addition, he
is the scientific head of the fourth research axis entitled “AI for smart and secure territories”. Two PhD
thesis students have been funded by this program: Tong Zhao (Ecole des Ponts ParisTech, master MVA,
now at Dassault Systemes Paris) and Nissim Maruani (Ecole Polytechnique, Master MVA, first year PhD
student).

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Inria Annual Report 2023

9.3.2 Inria challenge ROAD-AI - with CEREMA

The road network is one of the most important elements of public heritage. Road operators are responsible
for maintaining, operating, upgrading, replacing and preserving this heritage, while ensuring careful
management of budgetary and human resources. Today, the infrastructure is being severely tested by
climate change (increased frequency of extreme events, particularly flooding and landslides). Users are
also extremely attentive to issues of safety and comfort linked to the use of infrastructure, as well as to
environmental issues relating to infrastructure construction and maintenance. Roads and structures
are complex systems: numerous sub-systems, non-linear evolution of intrinsic characteristics, assets
subject to extreme events, strong changes in use or constraints (e.g. climate change) due to their very
long lifespan. This Inria challenge takes into account the following difficulties : volume and nature of data
(heterogeneity and incompleteness, multi-sources... ), multiple scales of analysis (from the sub-systems
of an asset to the complete national network), prediction of very local behaviors on the scale of a global
network, complexity of road objects due to their operation and weather conditions, 3D modeling with
semantization, and expert knowledge modeling. This Inria challenge aims at providing the scientific
building blocks for upstream data acquisition and downstream decision support. Cerema’s use cases for
this challenge separate into four parts:

• Build a dynamic “digital twin” of the road and its environment on the scale of a complete network;

• the behavioral "laws" of pavements and engineering structures using data from surface monitoring

or structure visits, sensors and environmental data;

• Invent the concept of connected bridges and tunnels on a system scale;

• Define strategic investment and maintenance planning methods (predictive, prescriptive and

autonomous).

9.3.3 PEPR NumPex

The Digital PEPR for the Exascale (NumPEx) aims to design and develop the software components and
tools that will equip future exascale machines and to prepare the major application domains to fully
exploit the capabilities of these machines. These major application domains include both scientific
research and the industrial sector. This project therefore contributes to France’s response to the next
EuroHPC call for expressions of interest (AMI) (Exascale France Project), with a view to hosting one of
the two European exascale machines planned in Europe by 2024. The French consortium has chosen
GENCI as its "Hosting Entity" and the CEA TGCC as its "Hosting Center". This PEPR contributes to the
constitution of a set of tools, software, applications but also training that will allow France to remain one
of the leaders in the field by developing a national ecosystem for Exascale coordinated with the European
strategy.

Our team is mainly involved in the first workpackage entitled “Model and discretize at Exascale”. The
main motivation is that geometric representations and their discrete counterparts (such as meshes) are
usually the starting point for simulation. These include adaptive, possibly multiresolution, robust to
defects, and efficient parallel representations of large-scale models. The physics-based models need to
include multiple phenomena, or process couplings at multiple scales in space and time. Space and time
adaptivity are then mandatory. Time integration requires special care to become more parallel, more
asynchronous, and more accurate for long-time simulations. The requirement of adaptivity in geometry
and physics creates an imbalance that needs to be overcome. AI-driven, data-driven, reduced-order, and
more generally surrogate models are now mandatory and take various forms. Data must be understood in
a broad sense: from observations of the real physical system to synthetic data generated by the physical
models. Surrogates enable orders of magnitude faster model evaluations through the extraction and com-
pression of salient features in a very intensive training/learning (supervised, unsupervised or reinforced)
phase. Research topics include: handling multiphysics and multiscale coupling, learning parametric
dependencies, differential operators or underlying physical laws, mitigating intensive communications
during the compression process, and exploiting the underlying computer and data architectures. Multi-
fidelity models include hierarchies of models to provide a multi-fidelity problem-solving approach to
address very intensive problems beyond the current computing capabilities. The challenge is to switch

Project TITANE

31

between representations to efficiently compute and handle bias in so-called “many-query” problems,
which include design, optimization, UQ and other high dimensional PDE problems.

For our team, this project is funding a postdoctoral fellow (to be recruited in 2024), in collaboration

with Strasbourg University.

10 Dissemination

Participants:

Pierre Alliez, Florent Lafarge, Roberto Dyke, Armand Zampieri.

10.1 Promoting scientific activities

10.1.1 Scientific events: organisation

Member of the organizing committees

• Pierre Alliez was an advisory board member for the EUROGRAPHICS 2023 conference.

• Pierre Alliez was a member of the scientific committee for the SophIA Summit conference 2023.

• Pierre Alliez organized the third Inria-DFKI summer school on AI, at Inria Sophia Antipolis (around

100 participants), in collaboration with Roberto Dyke and Armand Zampieri.

10.1.2 Scientific events: selection

Member of the conference program committees Pierre Alliez was a committee member of the Euro-
graphics Symposium on Geometry Processing 2023.

Reviewer

• Pierre Alliez was a reviewer for ACM SIGGRAPH, ACM SIGGRAPH Asia and the Eurographics

Symposium on Geometry Processing.

• Florent Lafarge was a reviewer for CVPR, ICCV, ACM SIGGRAPH and ACM SIGGRAPH ASIA.

10.1.3 Journal

Member of the editorial boards

• Pierre Alliez is the editor in chief of the Computer Graphics Forum (CGF), in tandem with Helwig
Hauser, since January 2022 and for a four-year term. CGF is one of the leading journals in Computer
Graphics and the official journal of the Eurographics association.

• Pierre Alliez is a member of the editorial board of the CGAL open source project.

• Florent Lafarge is an associate editor for the ISPRS Journal of Photogrammetry and Remote Sensing

and for the Revue Française de Photogrammétrie et de Télédétection.

Reviewer - reviewing activities

• Florent Lafarge was a reviewer for IEEE Transactions on Pattern Analysis and Machine Intelligence

(TPAMI).

• Pierre Alliez declined most invitations for reviewing journal papers, since he took the role of editor

in chief of the Computer Graphics Forum.

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Inria Annual Report 2023

10.1.4 Leadership within the scientific community

Pierre Alliez is a member of the following steering committees: Eurographics (via his role of editor-in
chief of the Computer Graphics Forum journal), Eurographics Symposium on Geometry Processing and
Eurographics Workshop on Graphics and Cultural Heritage (until September 2023). He was chair of the
Ph.D. award committee of the Eurographics Association from 2020 to 2023.

10.1.5 Scientific expertise

• Pierre Alliez was a reviewer for the ERC and Eureka programs. He is a scientific advisory board
member for the Bézout Labex in Paris (Models and algorithms: from the discrete to the continuous).

• Florent Lafarge was a reviewer for the MESR (CRI - crédit impot recherche and JEI - jeunes entre-

prises innovantes).

10.1.6 Research administration

• Pierre Alliez is president of the Inria Evaluation Committee since September 2023, in collabotation

with Christine Morin and Luce Brotcorne. This role is a half-time position.

• Pierre Alliez is scientific head for the Inria-DFKI partnership since September 2022.

• Pierre Alliez is a member of the scientific committee of the 3IA Côte d’Azur.

• Florent Lafarge is a member of the NICE committee. The main actions of the NICE committee
are to verify the scientific aspects of the files of postdoctoral students, to give scientific opinions
on candidates for national campaigns for postdoctoral stays, delegations, secondments as well as
requests for long duration invitations.

• Florent Lafarge is a member of the technical committee of the UCA Academy of Excellence "Space,

Environment, Risk and Resilience".

10.2 Teaching - Supervision - Juries

10.2.1 Teaching

• Master: Pierre Alliez (with Xavier Descombes, Marc Antonini and Laure Blanc-Féraud), advanced

machine learning, 12h, M2, Université Côte d’Azur, France.

• Master: Pierre Alliez (with Gaétan Bahl and Armand Zampieri), deep learning, 32h, M2, Université

Côte d’Azur, France.

• Master: Florent Lafarge, Applied AI, 8h, M2, Université Côte d’Azur, France.

• Master: Florent Lafarge (with Angelos Mantzaflaris), Numerical Interpolation, 55h, M1, Université

Côte d’Azur, France.

10.2.2 Supervision

• PhD defended in March 2023: Tong Zhao, Progressive shape reconstruction from raw 3D point
clouds, co-advised by Pierre Alliez and Laurent Busé (Aromath Inria project-team), funded by 3IA
Côte d’Azur, in collaboration with Jean-Marc Thiery and Tamy Boubekeur (formerly at Telecom
ParisTech, now at Adobe research), and David Cohen-Steiner (Datashape Inria project-team).

• PhD in progress: Rao Fu, Surface reconstruction from raw 3D point clouds, thesis funded by EU
project GRAPES, in collaboration with Geometry Factory, since January 2021, advised by Pierre
Alliez. Rao should defend in February 2024.

• PhD in progress: Jacopo Iollo, Sequential Bayesian Optimal Design, funded by Inria challenge
ROAD-AI, in collaboration with CEREMA, since January 2022, co-advised by Florence Forbes (Inria
Grenoble), Christophe Heinkele (CEREMA) and Pierre Alliez.

Project TITANE

33

• PhD in progress: Marion Boyer, Geometric modeling of urban scenes with LOD2 formalism from

satellite images, funded by CNES and Airbus, since October 2022, advised by Florent Lafarge.

• PhD in progress: Nissim Maruani, Learnable representations for 3D shapes, funded by 3IA, since
November 2022, co-advised by Pierre Alliez and Mathieu Desbrun (Geomerix Inria project-team,
Inria Saclay), and in collaboraton with Maks Ovsjanikov (Ecole Polytechnique).

• PhD in progress: Armand Zampieri, Compression and visibility of 3D point clouds, Cifre thesis with
Samp AI, since December 2022, co-advised by Pierre Alliez and Guillaume Delarue (Samp AI).

• PhD in progress: Moussa Bendjilali, From 3D point clouds to cognitive 3D models, Cifre thesis with

AlteIA, since September 2023, co-advised by Pierre Alliez and Nicola Luminari (AlteIA).

10.2.3 Juries

• Pierre Alliez was a reviewer for the HDR committee of Loïc Landrieu (IGN and Ecole des Ponts

ParisTech).

• Pierre Alliez was a HDR committee member for Frédéric Payan (I3S, Université Côte d’Azur).

• Pierre Alliez chaired the PhD thesis committee of Morten Pedersen (University of Copenhagen and

Epione project-team, Inria center at Université Côte d’Azur).

• Pierre Alliez was a member of the "Comité de Suivi Doctoral" for three Phd theses: Di Yang (Stars
Inria project-team), Arnaud Gueze (Ecole Polytechnique) and Stefan Larsen (Acentauri Inria project-
team).

• Florent Lafarge was a reviewer for the PhD of Mariem Mezghanni (LIX).

• Florent Lafarge was a member of the "Comité de Suivi Doctoral" for the PhD thesis of Amine Ouasfi

(Mimetic Inria project-team) and Grégoire Grzeczkowicz (IGN).

11 Scientific production

11.1 Major publications

[1]

J.-P. Bauchet and F. Lafarge. ‘Kinetic Shape Reconstruction’. In: ACM Transactions on Graphics
(2020). This project was partially funded by Luxcarta Technology. We thank our anonymous review-
ers for their input, Qian-Yi Zhou and Arno Knapitsch for providing us datasets from the Tanks and
Temples benchmark (Meeting Room,Horse,M60,Barn,Ignatius,Courthouse and Church), and Pierre
Alliez, Mathieu Desbrun and George Drettakis for their help and advice. We are also grateful to
Liangliang Nan and Hao Fang for sharing comparison materials. Datasets Full thing,Castle,Tower of
Piand Hilbert cube originate from Thingi 10K,Hand, Rocker Arm, Fertility and LansfromAim@Shape,
and Stanford Bunny and Asian Dragon from the Stanford 3D Scanning Repository. DOI: 10.1145/3
376918. URL: https://hal.inria.fr/hal-02924409.

[2] M. Budninskiy, B. Liu, F. De Goes, Y. Tong, P. Alliez and M. Desbrun. ‘Optimal Voronoi Tessellations
with Hessian-based Anisotropy’. In: ACM Transactions on Graphics. Proceedings of SIGGRAPH Asia
(Dec. 2016), p. 12. URL: https://hal.inria.fr/hal-01376243.

[3] L. Duan and F. Lafarge. ‘Towards large-scale city reconstruction from satellites’. In: European
Conference on Computer Vision (ECCV). Amsterdam, Netherlands, Oct. 2016. URL: https://hal.i
nria.fr/hal-01352466.

[4] L. Feng, P. Alliez, L. Busé, H. Delingette and M. Desbrun. ‘Curved Optimal Delaunay Triangulation’.
In: ACM Transactions on Graphics. Proceedings of SIGGRAPH 2018 37.4 (Aug. 2018), p. 16. DOI:
10.1145/3197517.3201358. URL: https://hal.inria.fr/hal-01826055.

[5] M. Li, F. Lafarge and R. Marlet. ‘Approximating shapes in images with low-complexity polygons’. In:
CVPR 2020 - IEEE Conference on Computer Vision and Pattern Recognition. Seattle / Virtual, United
States, June 2020. URL: https://hal.inria.fr/hal-02526028.

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Inria Annual Report 2023

[6] M. Mandad, D. Cohen-Steiner and P. Alliez. ‘Isotopic Approximation within a Tolerance Volume’.
In: ACM Transactions on Graphics. Proceedings of SIGGRAPH 34.4 (2015), p. 12. DOI: 10.1145/276
6950. URL: https://hal.inria.fr/hal-01186074.

[7] M. Mandad, D. Cohen-Steiner, L. Kobbelt, P. Alliez and M. Desbrun. ‘Variance-Minimizing Transport
Plans for Inter-surface Mapping’. In: ACM Transactions on Graphics 36 (2017), p. 14. DOI: 10.1145
/3072959.3073671. URL: https://hal.inria.fr/hal-01519006.

[8] C. Portaneri, M. Rouxel-Labbé, M. Hemmer, D. Cohen-Steiner and P. Alliez. ‘Alpha Wrapping with
an Offset’. In: ACM Transactions on Graphics. Proceedings of the ACM SIGGRAPH conference 41.4
(1st June 2022), pp. 1–22. DOI: 10.1145/3528223.3530152. URL: https://hal.inria.fr/hal-
03688637.

[9] M. Rouhani, F. Lafarge and P. Alliez. ‘Semantic Segmentation of 3D Textured Meshes for Urban
Scene Analysis’. In: ISPRS Journal of Photogrammetry and Remote Sensing 123 (2017), pp. 124–139.
DOI: 10.1016/j.isprsjprs.2016.12.001. URL: https://hal.inria.fr/hal-01469502.

[10] Y. Verdie, F. Lafarge and P. Alliez. ‘LOD Generation for Urban Scenes’. In: ACM Transactions on

Graphics 34.3 (2015), p. 15. URL: https://hal.inria.fr/hal-01113078.

[11] T. Zhao, L. Busé, D. Cohen-Steiner, T. Boubekeur, J.-M. Thiery and P. Alliez. ‘Variational Shape
Reconstruction via Quadric Error Metrics’. In: SIGGRAPH 2023 - The 50th International Conference
& Exhibition On Computer Graphics & Interactive Techniques. Los Angeles, United States, 6th Aug.
2023. DOI: 10.1145/3588432.3591529. URL: https://inria.hal.science/hal-04131765.

11.2 Publications of the year

International journals

[12] M. Trzeciak, K. Pluta, Y. Fathy, L. Alcalde, S. Chee, A. Bromley, I. Brilakis and P. Alliez. ‘ConSLAM:
Construction Data Set for SLAM’. In: Journal of Computing in Civil Engineering 37.3 (May 2023).
DOI: 10.1061/JCCEE5.CPENG-5212. URL: https://inria.hal.science/hal-04039392.

[13] V. Vidal, L. Dubouchet, G. Lavoué and P. Alliez. ‘Progressive Compression of Triangle Meshes’. In:
Image Processing On Line 13 (2023), pp. 1–21. DOI: 10.5201/ipol.2023.418. URL: https://inr
ia.hal.science/hal-03924042.

[14] T. Zhao, M. Yu, P. Alliez and F. Lafarge. ‘Sharp Feature Consolidation from Raw 3D Point Clouds
via Displacement Learning’. In: Computer Aided Geometric Design 13 (June 2023), p. 102204. DOI:
10.1016/j.cagd.2023.102204. URL: https://inria.hal.science/hal-03747150.

[15] D. Zint, N. Maruani, M. Rouxel-Labbé and P. Alliez. ‘Feature-Preserving Offset Mesh Generation
from Topology-Adapted Octrees’. In: Computer Graphics Forum. Proceedings of Eurographics
Symposium on Geometry Processing 2023 42.5 (2023), p. 12. DOI: 10.1111/cgf.14906. URL:
https://inria.hal.science/hal-04135266.

International peer-reviewed conferences

[16] R. Fu, C. Wen, Q. Li, X. Xiao and P. Alliez. ‘BPNet: Bézier Primitive Segmentation on 3D Point Clouds’.
In: IJCAI 2023 - 32nd International Joint Conference on Artificial Intelligence. MACAO, Macau SAR
China: International Joint Conferences on Artificial Intelligence Organization, 2023, pp. 1–9. DOI:
10.24963/ijcai.2023/84. URL: https://hal.science/ineris-04151504.

[17] N. Maruani, R. Klokov, M. Ovsjanikov, P. Alliez and M. Desbrun. ‘VoroMesh: Learning Watertight
Surface Meshes with Voronoi Diagrams’. In: ICCV 2023 - International Conference on Computer
Vision. Paris, France, 28th Aug. 2023. URL: https://inria.hal.science/hal-04199011.

[18] T. Zhao, L. Busé, D. Cohen-Steiner, T. Boubekeur, J.-M. Thiery and P. Alliez. ‘Variational Shape
Reconstruction via Quadric Error Metrics’. In: SIGGRAPH 2023 - The 50th International Conference
& Exhibition On Computer Graphics & Interactive Techniques. Los Angeles, United States, 6th Aug.
2023. DOI: 10.1145/3588432.3591529. URL: https://inria.hal.science/hal-04131765.

Project TITANE

35

Conferences without proceedings

[19] P. Monasse, R. Djahel and B. Vallet. ‘Registration for Urban Modeling Based on Linear and Planar
Features’. In: EUVIP 2023 - 11th European Workshop on Visual Information Processing. Gjovik,
Norway, 2023. URL: https://enpc.hal.science/hal-04217550.

Doctoral dissertations and habilitation theses

[20] T. Zhao. ‘Progressive shape reconstruction from raw 3D point clouds’. Université Côte d’Azur,

27th Mar. 2023. URL: https://inria.hal.science/tel-04131762.

Reports & preprints

[21] R. Sulzer, L. Landrieu, R. Marlet and B. Vallet. A Survey and Benchmark of Automatic Surface

Reconstruction from Point Clouds. 2023. URL: https://hal.science/hal-03968453.

Other scientific publications

[22]

J. Lussange, M. Yu, Y. Tarabalka and F. Lafarge. 3D detection of roof sections from a single satellite
image and application to LOD2-building reconstruction. July 2023. URL: https://inria.hal.sci
ence/hal-04343905.

11.3 Other

Softwares

[23]

[SW ] D. Anisimov, D. Bommes, K. Hormann and P. Alliez, 2D Generalized Barycentric Coordinates
version C++, 13th Jan. 2023. LIC: GNU General Public License v3.0 or later. HAL: 〈hal-03938534〉,
URL: https://inria.hal.science/hal-03938534, VCS: https://github.com/CGAL/cgal
/tree/master/Barycentric\_coordinates\_2, SWHID: 〈swh:1:dir:695cebe5d4ab078642
bb746399b7372b62d9dc77;origin=https://github.com/CGAL/cgal;visit=swh:1:snp:0
8830c7520f20568b2ce9e0b4b05ce76c9012817;anchor=swh:1:rev:1d6830501d42d6f4647
79ea632f0944a424560f5;path=/Barycentric\_coordinates\_2/〉.
<scratchpad>
- Main topic: Geometric Modeling of 3D Environments
- Key points:
  - The project TITANE aims to create a geometric model of complex scenes from physical measurements.
  - The research focuses on perception, cognition, and interaction in the context of geometric modeling.
  - The project involves various aspects, including analysis, reconstruction, approximation, and application domains.
  - The research program includes context, analysis, approximation, and application domains.
  - The project has collaborations with various industries and organizations, such as CNES, Airbus, and Samp AI.
- Structure: The document is organized into sections, including activity report, project-team, research program, application domains, highlights of the year, and so on.
- Potential artifacts to ignore: Links, HTML tags, and other web-related elements.
</scratchpad>

<final_summary>
The project TITANE is focused on geometric modeling of 3D environments, with a specific emphasis on perception, cognition, and interaction. The research program involves various aspects, including analysis, reconstruction, approximation, and application domains. The project has collaborations with various industries and organizations, such as CNES, Airbus, and Samp AI. The research aims to contribute to the development of algorithms with theoretical foundations, with the ultimate goal of making geometry modeling and processing routine for practitioners who deal with real-world data.
The project TITANE is focused on geometric modeling of 3D environments, with a specific emphasis on perception, cognition, and interaction. The research program involves various aspects, including analysis, reconstruction, approximation, and application domains. The project has collaborations with various industries and organizations, such as CNES, Airbus, and Samp AI. The research aims to contribute to the development of algorithms with theoretical foundations, with the ultimate goal of making geometry modeling and processing routine for practitioners who deal with real-world data.