Papers › PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud
Shaoshuai Shi, Xiaogang Wang, Hongsheng Li
In this paper, we propose PointRCNN for 3D object detection from raw point cloud. The whole framework is composed of two stages: stage-1 for the bottom-up 3D proposal generation and stage-2 for refining proposals in the canonical coordinates to obtain the final detection results. Instead of generating proposals from RGB image or projecting point cloud to bird's view or voxels as previous methods do, our stage-1 sub-network directly generates a small number of high-quality 3D proposals from point cloud in a bottom-up manner via segmenting the point cloud of the whole scene into foreground points and background. The stage-2 sub-network transforms the pooled points of each proposal to canonical coordinates to learn better local spatial features, which is combined with global semantic features of each point learned in stage-1 for accurate box refinement and confidence prediction. Extensive experiments on the 3D detection benchmark of KITTI dataset show that our proposed architecture outperforms state-of-the-art methods with remarkable margins by using only point cloud as input. The code is available at https://github.com/sshaoshuai/PointRCNN.
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Code
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Code Syntology ran Syntology
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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 | PointRCNN | AP | 84.32% | #19 of 26 | Archive leaderboard | report |
| 3D Object Detection | KITTI Cars Hard | PointRCNN | AP | 67.86% | #18 of 25 | Archive leaderboard | report |
| 3D Object Detection | KITTI Cyclists Easy | PointRCNN | AP | 73.93% | #8 of 12 | Archive leaderboard | report |
| 3D Object Detection | KITTI Cyclists Hard | PointRCNN | AP | 53.59% | #7 of 12 | Archive leaderboard | report |
| 3D Object Detection | KITTI Cyclists Moderate | PointRCNN | AP | 59.60% | #7 of 13 | Archive leaderboard | report |
| Object Detection | KITTI Cars Easy | PointRCNN Shi et al. (2019) | AP | 85.94 | #2 of 5 | Archive leaderboard | report |
| Object Detection | KITTI Cars Hard | PointRCNN Shi et al. (2019) | AP | 68.32 | #2 of 5 | Archive leaderboard | report |
| Object Detection | KITTI Cars Moderate | PointRCNN Shi et al. (2019) | AP | 75.76 | #2 of 4 | Archive leaderboard | report |
| Robust 3D Object Detection | KITTI-C | PointRCNN | mean Corruption Error (mCE) | 91.88% | #2 of 5 | 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.
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