Abstract
Grasping is natural for humans. However, it involves complex hand configurations and soft tissue deformation that can result in complicated regions of contact between the hand and the object. Understanding and modeling this contact can potentially improve hand models, AR/VR experiences, and robotic grasping. Yet, we currently lack datasets of hand-object contact paired with other data modalities, which is crucial for developing and evaluating contact modeling techniques. We introduce ContactPose, the first dataset of hand-object contact paired with hand pose, object pose, and RGB-D images. ContactPose has 2306 unique grasps of 25 household objects grasped with 2 functional intents by 50 participants, and more than 2.9 M RGB-D grasp images. Analysis of ContactPose data reveals interesting relationships between hand pose and contact. We use this data to rigorously evaluate various data representations, heuristics from the literature, and learning methods for contact modeling. Data, code, and trained models are available at https://contactpose.cc.gatech.edu.
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Acknowledgements
We are thankful to the anonymous reviewers for helping improve this paper. We would also like to thank Elise Campbell, Braden Copple, David Dimond, Vivian Lo, Jeremy Schichtel, Steve Olsen, Lingling Tao, Sue Tunstall, Robert Wang, Ed Wei, and Yuting Ye for discussions and logistics help.
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Brahmbhatt, S., Tang, C., Twigg, C.D., Kemp, C.C., Hays, J. (2020). ContactPose: A Dataset of Grasps with Object Contact and Hand Pose. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12358. Springer, Cham. https://doi.org/10.1007/978-3-030-58601-0_22
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