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

Authors :
You, Yang
Lou, Yujing
Li, Chengkun
Cheng, Zhoujun
Li, Liangwei
Ma, Lizhuang
Wang, Weiming
Lu, Cewu
Publication Year :
2020

Abstract

Detecting 3D objects keypoints is of great interest to the areas of both graphics and computer vision. There have been several 2D and 3D keypoint datasets aiming to address this problem in a data-driven way. These datasets, however, either lack scalability or bring ambiguity to the definition of keypoints. Therefore, 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. Our code and data are available on https://github.com/qq456cvb/KeypointNet.<br />Comment: 8 pages; to appear in CVPR 2020

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2002.12687
Document Type :
Working Paper