| Paper ID | IVMSP-15.2 |
| Paper Title |
VK-Net: Category-level Point Cloud Registration with Unsupervised Rotation Invariant Keypoints |
| Authors |
Zhi Chen, Wei Yang, Zhenbo Xu, Zhenbo Shi, Liusheng Huang, University of Science and Technology of China, China |
| Session | IVMSP-15: Local Descriptors and Texture |
| Location | Gather.Town |
| Session Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation Time: | Wednesday, 09 June, 15:30 - 16:15 |
| Presentation |
Poster
|
| Topic |
Image, Video, and Multidimensional Signal Processing: [IVELI] Electronic Imaging |
| IEEE Xplore Open Preview |
Click here to view in IEEE Xplore |
| Virtual Presentation |
Click here to watch in the Virtual Conference |
| Abstract |
In this paper, we propose VK-Net, a neural network that learns to discover a set of category-specific keypoints from a single point cloud in an unsupervised manner. VK-Net is able to generate semantically consistent and rotation invariant keypoints across objects of the same category and different views. Particularly, we find that utilizing learned keypoints for the task of point cloud registration outperforms other traditional and learning-based approaches. Given the paired source and target point clouds, we can construct keypoint correspondences from learned keypoints using VK-Net. These keypoint correspondences are then employed to calculate a good pose initialization, after which an ICP is utilized to refine the registration. Extensive experiments on the ShapeNet dataset demonstrate that our model outperforms the state-of-the-art methods by a large margin. |
