Datasets:

MTJester
/

testCHIP

license: cc-by-nc-sa-4.0
tags:
  - 6d-pose-estimation
size_categories:
  - 10K<n<100K
dataset_info:
  features:
    - name: scene_id
      dtype: string
    - name: image_id
      dtype: string
    - name: camera_type
      dtype: string
    - name: image
      dtype: image
    - name: depth
      dtype: image
    - name: width
      dtype: int64
    - name: height
      dtype: int64
    - name: split
      dtype: string
    - name: labels
      dtype: string
    - name: camera_params
      dtype: string
    - name: source_image_id
      dtype: string
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/*
pretty_name: CHIP

CHIP: A multi-sensor dataset for 6D pose estimation of chairs in industrial settings

🏠 Homepage

📄 Paper

Introduction

Accurate 6D pose estimation of complex objects in 3D environments is essential for effective robotic manipulation. Yet, existing benchmarks fall short in evaluating 6D pose estimation methods under realistic industrial conditions, as most datasets focus on household objects in domestic settings, while the few available industrial datasets are limited to artificial setups with objects placed on tables. To bridge this gap, we introduce CHIP, the first dataset designed for 6D pose estimation of chairs manipulated by a robotic arm in a real-world industrial environment. CHIP includes seven distinct chairs captured using three different RGBD sensing technologies and presents unique challenges, such as distractor objects with fine-grained differences and severe occlusions caused by the robotic arm and human operators. CHIP comprises 77,811 RGBD images annotated with ground-truth 6D poses automatically derived from the robot's kinematics, averaging 11,115 annotations per chair. We benchmark CHIP using three zero-shot 6D pose estimation methods, assessing performance across different sensor types, localization priors, and occlusion levels. Results show substantial room for improvement, highlighting the unique challenges posed by the dataset.

Dataset Summary

Number of images: 77,811 RGBD images
Number of object classes: 7 distinct chair models
Sensors used: Intel RealSense D435, Intel RealSense L515, Stereo Labs ZED
Annotations: Ground-truth 6D poses derived from robot kinematics (~11,115 annotations per chair)
Occlusion levels: No occlusions, moderate occlusions

Object Classes

CHIP includes seven high-quality chair models from Andreu World, covering a variety of structures and materials.

Frameonly designs:

000001: Smile si0325 — Andreu World link
000003: Carlotta si0991 — Andreu World link
000006: Carola so0903 — Andreu World link
000007: Rizo so2043 — Andreu World link

Solid-wood designs:

000002: Happy si0374 — Andreu World link
000004: Duos si2750 — Andreu World link
000005: Rdl si7291 — Andreu World link

Data Fields

- scene_id: Unique identifier for each scene in the dataset (BOP format).
- image_id: Unique identifier for each image within a scene and camera type (BOP format).
- camera_type: Type of camera used to capture the image (e.g., 'zed', 'rs_l515', 'rs_d435').
- image: RGB image captured by the specified camera.
- depth: Depth image corresponding to the RGB image, captured by the specified camera.
- width: Width of the image in pixels.
- height: Height of the image in pixels.
- split: Dataset split to which the image belongs (e.g., 'test_no_occlusions', 'test_moderate_occlusions').
- source_image_id: Original image identifier from the CHIP dataset.
- labels: JSON string containing object annotations, including 6D poses and visibility information.
- camera_params: JSON string containing intrinsic and extrinsic camera parameters for the specified camera.

Uses

The CHIP dataset can be used for a wide range of research tasks, including:

Benchmarking 6D pose estimation in realistic industrial environments
Evaluating robustness to challenging conditions, such as occlusions, clutter, and visually similar distractor objects
Studying cross-sensor generalization across LiDAR-based, passive-stereo, and active-stereo RGB-D sensors
6D pose tracking during robotic manipulation, including motion sequences with dynamic occlusions

Citation

If you find CHIP useful for your work please cite:

@inproceedings{nardon2025chip,
  title={CHIP: A multi-sensor dataset for 6D pose estimation of chairs in industrial settings},
  author={Nardon, Mattia and Mujika Agirre, Mikel and González Tomé, Ander and Sedano Algarabel, Daniel and Rueda Collell, Josep and Caro, Ana Paola and Caraffa, Andrea and Poiesi, Fabio and Chippendale, Paul Ian and Boscaini, Davide},
  booktitle={British Machine Vision Conference (BMVC)},
  year={2025}}

Acknowledgement

This work was supported by the European Union's Horizon Europe research and innovation programme under grant agreement No. 101058589 (AI-PRISM).

Partners

Contact

For any questions regarding the dataset, please contact Mattia Nardon (mattia.nardon@fbk.eu).

Homepage link: https://tev-fbk.github.io/CHIP/

Paper link: https://arxiv.org/abs/2506.09699