Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes
Published in CVPR, 2022
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, a 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 challenging large-scale datasets of real point cloud scans: ScanNet, SceneNN with 11.4 and 5.3 mAP improvement respectively.
Recommended citation: You, Y., Ye, Z., Lou, Y., Li, C., Li, Y. L., Ma, L., ... & Lu, C. (2020). Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes. arXiv preprint arXiv:2011.12001.