KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations
Published in CVPR, 2020
We present KeypointNet: the first large-scale and diverse 3D keypoint dataset that contains 83,231 keypoints and 8,329 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.
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).