  We present a novel and high-performance 3D object detection framework, named
PointVoxel-RCNN (PV-RCNN), for accurate 3D object detection from point clouds.
Our proposed method deeply integrates both 3D voxel Convolutional Neural
Network (CNN) and PointNet-based set abstraction to learn more discriminative
point cloud features. It takes advantages of efficient learning and
high-quality proposals of the 3D voxel CNN and the flexible receptive fields of
the PointNet-based networks. Specifically, the proposed framework summarizes
the 3D scene with a 3D voxel CNN into a small set of keypoints via a novel
voxel set abstraction module to save follow-up computations and also to encode
representative scene features. Given the high-quality 3D proposals generated by
the voxel CNN, the RoI-grid pooling is proposed to abstract proposal-specific
features from the keypoints to the RoI-grid points via keypoint set abstraction
with multiple receptive fields. Compared with conventional pooling operations,
the RoI-grid feature points encode much richer context information for
accurately estimating object confidences and locations. Extensive experiments
on both the KITTI dataset and the Waymo Open dataset show that our proposed
PV-RCNN surpasses state-of-the-art 3D detection methods with remarkable margins
by using only point clouds. Code is available at
https://github.com/open-mmlab/OpenPCDet.
