PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection [article]

Shaoshuai Shi, Li Jiang, Jiajun Deng, Zhe Wang, Chaoxu Guo, Jianping Shi, Xiaogang Wang, Hongsheng Li
2022 arXiv   pre-print
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 3D object detection from point clouds. First, we propose a novel 3D detector, PV-RCNN, which consists of two steps: the voxel-to-keypoint scene encoding and keypoint-to-grid RoI feature abstraction. These two steps deeply integrate the 3D voxel CNN with the
more » ... t-based set abstraction for extracting discriminative features. Second, we propose an advanced framework, PV-RCNN++, for more efficient and accurate 3D object detection. It consists of two major improvements: the sectorized proposal-centric strategy for efficiently producing more representative keypoints, and the VectorPool aggregation for better aggregating local point features with much less resource consumption. With these two strategies, our PV-RCNN++ is more than 2x faster than PV-RCNN, while also achieving better performance on the large-scale Waymo Open Dataset with 150m * 150m detection range. Also, our proposed PV-RCNNs achieve state-of-the-art 3D detection performance on both the Waymo Open Dataset and the highly-competitive KITTI benchmark. The source code is available at https://github.com/open-mmlab/OpenPCDet.
arXiv:2102.00463v2 fatcat:s36oc3krvne7pgmqy4pn2tlc64