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PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection
Shaoshuai Shi, Li Jiang, Jiajun Deng, Zhe Wang, Chaoxu Guo, Jianping Shi, Xiaogang Wang, Hongsheng Li
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 Point-Voxel Region-based Convolution Neural Networks (PV-RCNNs) for 3D object detection on point clouds. First, we propose a novel 3D detector, PV-RCNN, which boosts the 3D detection performance by deeply integrating the feature learning of both point-based set abstraction and voxel-based sparse convolution through two novel steps, i.e., the voxel-to-keypoint scene encoding and the keypoint-to-grid RoI feature abstraction. Second, we propose an advanced framework, PV-RCNN++, for more efficient and accurate 3D object detection. It consists of two major improvements: sectorized proposal-centric sampling for efficiently producing more representative keypoints, and VectorPool aggregation for better aggregating local point features with much less resource consumption. With these two strategies, our PV-RCNN++ is about 3× faster than PV-RCNN, while also achieving better performance. The experiments demonstrate that our proposed PV-RCNN++ framework achieves state-of-the-art 3D detection performance on the large-scale and highly-competitive Waymo Open Dataset with 10 FPS inference speed on the detection range of 150m * 150m.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Object Detection | KITTI Cars Easy | PV-RCNN++ | AP | 90.14% | #9 of 26 | Archive leaderboard | report |
| 3D Object Detection | KITTI Cars Easy val | PV-RCNN++ | AP | 92.57 | #2 of 11 | Archive leaderboard | report |
| 3D Object Detection | KITTI Cars Hard | PV-RCNN++ | AP | 77.15% | #4 of 25 | Archive leaderboard | report |
| 3D Object Detection | KITTI Cars Hard val | PV-RCNN++ | AP | 82.69 | #2 of 10 | Archive leaderboard | report |
| 3D Object Detection | KITTI Cars Moderate val | PV-RCNN++ | AP | 84.83 | #3 of 11 | Archive leaderboard | report |
| 3D Object Detection | Waymo Open Dataset | PV-RCNN++ | mAPH/L2 | 69.5 | #6 of 8 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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