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

• URL: http://arxiv.org/abs/2506.09699v1
• Date: Wed, 11 Jun 2025 13:13:31 GMT
• Title: CHIP: A multi-sensor dataset for 6D pose estimation of chairs in industrial settings
• Authors: Mattia Nardon, Mikel Mujika Agirre, Ander González Tomé, Daniel Sedano Algarabel, Josep Rueda Collell, Ana Paola Caro, Andrea Caraffa, Fabio Poiesi, Paul Ian Chippendale, Davide Boscaini,
• Abstract summary: CHIP is the first dataset designed for 6D pose estimation of chairs in a real-world industrial environment.<n> CHIP comprises 77,811 RGBD images annotated with ground-truth 6D poses automatically derived from the robot's kinematics.<n>Results show substantial room for improvement, highlighting the unique challenges posed by the dataset.
• Score: 4.310149395049504
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. CHIP will be publicly released.

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