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.

本文提出了一种针对点云的三维目标检测新方法PV-RCNN，通过点和体素两个方面来深度整合特征学习，采用体素到关键点场景编码和关键点到网格RoI特征抽象两个创新步骤，更高效准确地实现三维目标检测。PV-RCNN++框架通过部分区域划分建议-中心采样和VectorPool池化等两种策略，在更低资源消耗下实现了更优秀的性能，相比PV-RCNN在保持准确率的同时提高了3倍速度，并在大规模Waymo开放数据集上取得了最先进的三维检测性能。 

PV-RCNN++：具有局部向量表示的点-体素特征集抽象 用于3D目标检测

PV-RCNN++：具有局部向量表示的点-体素特征集抽象用于3D目标检测