  In this paper, we propose PointRCNN for 3D object detection from raw point
cloud. The whole framework is composed of two stages: stage-1 for the bottom-up
3D proposal generation and stage-2 for refining proposals in the canonical
coordinates to obtain the final detection results. Instead of generating
proposals from RGB image or projecting point cloud to bird's view or voxels as
previous methods do, our stage-1 sub-network directly generates a small number
of high-quality 3D proposals from point cloud in a bottom-up manner via
segmenting the point cloud of the whole scene into foreground points and
background. The stage-2 sub-network transforms the pooled points of each
proposal to canonical coordinates to learn better local spatial features, which
is combined with global semantic features of each point learned in stage-1 for
accurate box refinement and confidence prediction. Extensive experiments on the
3D detection benchmark of KITTI dataset show that our proposed architecture
outperforms state-of-the-art methods with remarkable margins by using only
point cloud as input. The code is available at
https://github.com/sshaoshuai/PointRCNN.
