PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object DetectionDownload PDFOpen Website

2021, 00:00 (modified: 04 Nov 2022, 23:42)CoRR 2021Readers: Everyone
Abstract: 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 PointNet-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.
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