Name
Abstract | ||
|---|---|---|
3D object detection has attracted much attention thanks to the advances in sensors and deep learning methods for point clouds. Current state-of-the-art methods like VoteNet regress direct offset towards object centers and box orientations with an additional Multi-Layer-Perceptron network. Both their offset and orientation predictions are not accurate due to the fundamental difficulty in rotation classification. In the work, we disentangle the direct offset into Local Canonical Coordinates (LCC), box scales and box orientations. Only LCC and box scales are regressed, while box orientations are generated by a canonical voting scheme. Finally, an LCC-aware back-projection checking algorithm iteratively cuts out bounding boxes from the generated vote maps, with the elimination of false positives. Our model achieves state-of-the-art performance on three standard real-world benchmarks: ScanNet, SceneNN and SUN RGB-D. Our code is available on https://github.com/qq456cvb/CanonicalVoting. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/CVPR52688.2022.00126 | IEEE Conference on Computer Vision and Pattern Recognition |
Keywords | DocType | Volume |
Recognition: detection,categorization,retrieval, RGBD sensors and analytics, Scene analysis and understanding, Vision applications and systems | Conference | 2022 |
Issue | Citations | PageRank |
1 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yang You | 1 | 0 | 0.34 |
| Zelin Ye | 2 | 0 | 1.69 |
| Yujing Lou | 3 | 1 | 2.04 |
| Chengkun Li | 4 | 0 | 0.34 |
| Yonglu Li | 5 | 22 | 7.05 |
| Lizhuang Ma | 6 | 498 | 100.70 |
| Weiming Wang | 7 | 34 | 4.55 |
| Cewu Lu | 8 | 993 | 62.08 |