  LiDAR-based 3D detection in point cloud is essential in the perception system
of autonomous driving. In this paper, we present LiDAR R-CNN, a second stage
detector that can generally improve any existing 3D detector. To fulfill the
real-time and high precision requirement in practice, we resort to point-based
approach other than the popular voxel-based approach. However, we find an
overlooked issue in previous work: Naively applying point-based methods like
PointNet could make the learned features ignore the size of proposals. To this
end, we analyze this problem in detail and propose several methods to remedy
it, which bring significant performance improvement. Comprehensive experimental
results on real-world datasets like Waymo Open Dataset (WOD) and KITTI dataset
with various popular detectors demonstrate the universality and superiority of
our LiDAR R-CNN. In particular, based on one variant of PointPillars, our
method could achieve new state-of-the-art results with minor cost. Codes will
be released at https://github.com/tusimple/LiDAR_RCNN .
