  One of the main challenges in LiDAR-based 3D object detection is that the
sensors often fail to capture the complete spatial information about the
objects due to long distance and occlusion. Two-stage detectors with point
cloud completion approaches tackle this problem by adding more points to the
regions of interest (RoIs) with a pre-trained network. However, these methods
generate dense point clouds of objects for all region proposals, assuming that
objects always exist in the RoIs. This leads to the indiscriminate point
generation for incorrect proposals as well. Motivated by this, we propose Point
Generation R-CNN (PG-RCNN), a novel end-to-end detector that generates semantic
surface points of foreground objects for accurate detection. Our method uses a
jointly trained RoI point generation module to process the contextual
information of RoIs and estimate the complete shape and displacement of
foreground objects. For every generated point, PG-RCNN assigns a semantic
feature that indicates the estimated foreground probability. Extensive
experiments show that the point clouds generated by our method provide
geometrically and semantically rich information for refining false positive and
misaligned proposals. PG-RCNN achieves competitive performance on the KITTI
benchmark, with significantly fewer parameters than state-of-the-art models.
The code is available at https://github.com/quotation2520/PG-RCNN.
