  3D object detection is receiving increasing attention from both industry and
academia thanks to its wide applications in various fields. In this paper, we
propose the Point-Voxel Region based Convolution Neural Networks (PV-RCNNs) for
accurate 3D detection from point clouds. First, we propose a novel 3D object
detector, PV-RCNN-v1, which employs the voxel-to-keypoint scene encoding and
keypoint-to-grid RoI feature abstraction two novel steps. These two steps
deeply incorporate both 3D voxel CNN and PointNet-based set abstraction for
learning discriminative point-cloud features. Second, we propose a more
advanced framework, PV-RCNN-v2, for more efficient and accurate 3D detection.
It consists of two major improvements, where the first one is the sectorized
proposal-centric strategy for efficiently producing more representative and
uniformly distributed keypoints, and the second one is the VectorPool
aggregation to replace set abstraction for better aggregating local point-cloud
features with much less resource consumption. With these two major
modifications, our PV-RCNN-v2 runs more than twice as fast as the v1 version
while still achieving better performance on the large-scale Waymo Open Dataset
with 150m * 150m detection range. Extensive experiments demonstrate that our
proposed PV-RCNNs significantly outperform previous state-of-the-art 3D
detection methods on both the Waymo Open Dataset and the highly-competitive
KITTI benchmark.
