论文标题
规范投票：在3D场景中朝着强大的面向边界框检测
Canonical Voting: Towards Robust Oriented Bounding Box Detection in 3D Scenes
论文作者
论文摘要
由于传感器的进步和点云的深度学习方法，3D对象检测引起了很多关注。当前的最新方法（例如投票网）将通过额外的多层pecceptron网络将直接偏移偏向对象中心和盒子方向。由于旋转分类的基本困难，它们的偏移和方向预测都不准确。在工作中，我们将直接偏移量放在本地规范坐标（LCC），盒子秤和框方向上。只有LCC和盒子尺度会进行回归，而盒子方向是由规范投票方案产生的。最后，通过消除了误报，一种迭代的回检查算法迭代地迭代了界限。我们的模型在三个标准的现实世界基准：扫描仪，场景和Sun RGB-D上实现了最先进的性能。我们的代码可在https://github.com/qq456cvb/caronicalvoting上找到。
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.
