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


Mattia Nardon (Bruno Kessler Foundation), Mikel Mujika Agirre (Ikerlan), Ander González Tomé (Ikerlan), Daniel Sedano Algarabel (Ikerlan), Josep Rueda Collell (ikerlan), Ana Paola Caro (Andreu World), Andrea Caraffa (Bruno Kessler Foundation), Fabio Poiesi (Bruno Kessler Foundation), Paul Ian Chippendale (Bruno Kessler Foundation), Davide Boscaini (Bruno Kessler Foundation)
The 35th British Machine Vision Conference
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Abstract

Accurate 6D pose estimation of complex objects in 3D environments is crucial for effective robotic manipulation. However, existing benchmarks fall short in evaluating 6D pose estimation under realistic industrial conditions: most datasets focus on household objects in domestic settings, while the few available industrial datasets are limited to artificial scenarios 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 industrial environment. CHIP comprises seven distinct chairs recorded with three different RGBD sensing technologies and presents unique challenges, including distractor objects with fine-grained similarities and severe occlusions caused by the robotic arm and human operators. CHIP contains 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, evaluating their performance across different sensor types, localisation priors, and occlusion levels. Results reveal substantial room for improvement, highlighting the unique challenges posed by the dataset. Project page: https://tev-fbk.github.io/CHIP.

Citation

@inproceedings{Nardon_2025_BMVC,
author    = {Mattia Nardon and Mikel Mujika Agirre and Ander González Tomé and Daniel Sedano Algarabel and Josep Rueda Collell and Ana Paola Caro and Andrea Caraffa and Fabio Poiesi and Paul Ian Chippendale and Davide Boscaini},
title     = {CHIP: A multi-sensor dataset for 6D pose estimation of chairs in industrial settings},
booktitle = {36th British Machine Vision Conference 2025, {BMVC} 2025, Sheffield, UK, November 24-27, 2025},
publisher = {BMVA},
year      = {2025},
url       = {https://bmva-archive.org.uk/bmvc/2025/assets/papers/Paper_289/paper.pdf}
}


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