DexYCB: A Benchmark for Capturing Hand Grasping of Objects
By Yu-Wei Chao, Wei Yang, Yu Xiang, Pavlo Molchanov, Ankur Handa, Jonathan Tremblay, Yashraj S. Narang, Karl Van Wyk, Stan Birchfield, Jan Kautz and 1 more. Computer Vision and Pattern Recognition, 2021
463 citations recorded by the source corpus.
We introduce DexYCB, a new dataset for capturing hand grasping of objects. We first compare DexYCB with a related one through cross-dataset evaluation. We then present a thorough benchmark of state-of-the-art approaches on three relevant tasks: 2D object and keypoint detection, 6D object pose estimation, and 3D hand pose estimation. Finally, we evaluate a new robotics-relevant task: generating safe robot grasps in human-to-robot object handover. 1
Subfields: Computer Vision and Pattern Recognition, Control and Systems Engineering, Human-Computer Interaction
View Paper: https://www.semanticscholar.org/paper/1c620dbb3a9e2e1cf24df9cdd3d0b199b27a70b0
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In Computer Vision and Pattern Recognition
- Jitendra Malik
- Luc Van Gool
- Cordelia Schmid
- Trevor Darrell
- Andrew Zisserman
- Abhinav Kumar Gupta
- Andrea Vedaldi
- Dacheng Tao
In Control and Systems Engineering
- Angjoo Kanazawa
- Cordelia Schmid
- Gul Varol
- Xiaolong Wang
- Yuzhe Qin
- Jitendra Malik
- Ruihan Yang
- Ivan Laptev
In Human-Computer Interaction
- Jitendra Malik
- Angjoo Kanazawa
- Ilija Radosavovic
- Cordelia Schmid
- Georgios Pavlakos
- Ivan Laptev
- Gul Varol
- Dandan Shan
Citations in the corpus
- Towards Unconstrained Joint Hand-Object Reconstruction From RGB Videos (2021, background): “Existing datasets rely on depth data [10, 16], visible marker equipment [14, 52] or multiple views [6, 44, 56, 68].”
- Reconstructing Hands in 3D with Transformers (2023, methodology): “For training data, we use multiple available sources of data with hand annotations, including both studio/controlled datasets with 3D ground truth [6, 19, 40, 56, 63, 64], and in-the-wild datasets annotated with 2D keypoint locations [15, 25, 52].”
- DexMV: Imitation Learning for Dexterous Manipulation from Human Videos (2021, methodology): “2) Since we have no ground-truth pose annotation in our dataset, we evaluate the performance of hand pose estimation approaches on the DexYCB [18] dataset, which provides the pose ground-truths.”