KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations
This provides a scalable and less ambiguous dataset for researchers in graphics and computer vision working on 3D keypoint detection, though it is incremental as it builds on existing data-driven approaches.
The authors tackled the problem of 3D object keypoint detection by creating KeypointNet, a large-scale dataset with 103,450 keypoints and 8,234 3D models from 16 categories, aggregated from human annotations using a novel method to handle inconsistencies, and benchmarked ten state-of-the-art methods on it.
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.