Skeleton Merger: an Unsupervised Aligned Keypoint Detector

@article{Shi2021SkeletonMA,
  title={Skeleton Merger: an Unsupervised Aligned Keypoint Detector},
  author={Ruoxi Shi and Zhengrong Xue and Yang You and Cewu Lu},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2021},
  pages={43-52}
}
  • Ruoxi Shi, Zhengrong Xue, Cewu Lu
  • Published 19 March 2021
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Detecting aligned 3D keypoints is essential under many scenarios such as object tracking, shape retrieval and robotics. However, it is generally hard to prepare a high-quality dataset for all types of objects due to the ambiguity of keypoint itself. Meanwhile, current unsupervised detectors are unable to generate aligned keypoints with good coverage. In this paper, we propose an unsupervised aligned keypoint detector, Skeleton Merger, which utilizes skeletons to reconstruct objects. It is based… 

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