KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations

Published in CVPR, 2020

Yang You, Yujing Lou, Chengkun Li, Zhoujun Cheng, Liangwei Li, Lizhuang Ma, Cewu Lu, Weiming Wang

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Abstract

We present KeypointNet: the first large-scale and diverse 3D keypoint dataset that contains 103,450 keypoints and 8,234 3D models from 16 object categories, by leveraging numerous human annotations. To handle the inconsistency between annotations from different people, we propose a novel method to aggregate these keypoints automatically, through minimization of a fidelity loss. Finally, ten state-of-the-art methods are benchmarked on our proposed dataset.

BibTeX

@inproceedings{you2020keypointnet,
  title     = {KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations},
  author    = {Yang You and Yujing Lou and Chengkun Li and Zhoujun Cheng and Liangwei Li and Lizhuang Ma and Cewu Lu and Weiming Wang},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year      = {2020},
  eprint    = {2002.12687},
  archivePrefix = {arXiv}
}

Recommended citation: You, Y., Lou, Y., Li, C., Cheng, Z., Li, L., Ma, L., ... & Wang, W. (2020). KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 13647-13656).