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 this https URL

该论文提出了一种使用 Local Canonical Coordinates 和 canonical voting scheme 的 3D 目标检测方法，能够在三个标准实际数据集上取得最优性能