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[bibtex]@InProceedings{Jung_2024_CVPR, author = {Jung, HyunJun and Wu, Shun-Cheng and Ruhkamp, Patrick and Zhai, Guangyao and Schieber, Hannah and Rizzoli, Giulia and Wang, Pengyuan and Zhao, Hongcheng and Garattoni, Lorenzo and Meier, Sven and Roth, Daniel and Navab, Nassir and Busam, Benjamin}, title = {HouseCat6D - A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2024}, pages = {22498-22508} }
HouseCat6D - A Large-Scale Multi-Modal Category Level 6D Object Perception Dataset with Household Objects in Realistic Scenarios
Abstract
Estimating 6D object poses is a major challenge in 3D computer vision. Building on successful instance-level approaches research is shifting towards category-level pose estimation for practical applications. Current category-level datasets however fall short in annotation quality and pose variety. Addressing this we introduce HouseCat6D a new category-level 6D pose dataset. It features 1) multi-modality with Polarimetric RGB and Depth (RGBD+P) 2) encompasses 194 diverse objects across 10 household categories including two photometrically challenging ones and 3) provides high-quality pose annotations with an error range of only 1.35 mm to 1.74 mm. The dataset also includes 4) 41 large-scale scenes with comprehensive viewpoint and occlusion coverage 5) a checkerboard-free environment and 6. dense 6D parallel-jaw robotic grasp annotations. Additionally we present benchmark results for leading category-level pose estimation networks.
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