  Large imbalance often exists between the foreground points (i.e., objects)
and the background points in outdoor LiDAR point clouds. It hinders
cutting-edge detectors from focusing on informative areas to produce accurate
3D object detection results. This paper proposes a novel object detection
network by semantical point-voxel feature interaction, dubbed PV-RCNN++. Unlike
most of existing methods, PV-RCNN++ explores the semantic information to
enhance the quality of object detection. First, a semantic segmentation module
is proposed to retain more discriminative foreground keypoints. Such a module
will guide our PV-RCNN++ to integrate more object-related point-wise and
voxel-wise features in the pivotal areas. Then, to make points and voxels
interact efficiently, we utilize voxel query based on Manhattan distance to
quickly sample voxel-wise features around keypoints. Such the voxel query will
reduce the time complexity from O(N) to O(K), compared to the ball query.
Further, to avoid being stuck in learning only local features, an
attention-based residual PointNet module is designed to expand the receptive
field to adaptively aggregate the neighboring voxel-wise features into
keypoints. Extensive experiments on the KITTI dataset show that PV-RCNN++
achieves 81.60$\%$, 40.18$\%$, 68.21$\%$ 3D mAP on Car, Pedestrian, and
Cyclist, achieving comparable or even better performance to the
state-of-the-arts.
