论文标题:LiDAR R-CNN: An Efficient and Universal 3D Object Detector
作者单位:TuSimple
代码:https://github.com/tusimple/LiDAR_RCNN
论文:https://arxiv.org/pdf/2103.15297.pdf
一句话读论文:
解决点云稀疏性导致的proposal尺寸歧义问题。
Different from 2D RCNN, we should equip our LiDAR-RCNN with the ability to perceive the spacing and the size of proposals.
The simplest solution to the ambiguity problem is to normalize the point coordinates by the proposal size. If the proposal is enlarged, the point coordinates will be smaller and the size target will be higher. Consequently, the model could be aware of the size of the proposal.
When the R-CNN model is applied on multiple categories, it totally ignores the scale difference off different categories. The size normalization makes it more difficult for the model to distinguish different categories.
The points in it are still not aware of the voxel boundary. The model only have coarse information about the proposal size at voxel level, but not the point level. As a result, this solution alleviates, but not fully solves the ambiguity problem.
Revisiting the previous solutions, we can conclude that the key is to provide the size information to network, while preserving the shape of the object.
To provide the proposal boundary information, a simple way is to append the boundary offset to the point features. From the offset, the network will be able to know how far the points is from the proposal's boundary, which should solve the ambiguity problem.
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