Papers › Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations
Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations
Prarthana Bhattacharyya, Krzysztof Czarnecki
We present Deformable PV-RCNN, a high-performing point-cloud based 3D object detector. Currently, the proposal refinement methods used by the state-of-the-art two-stage detectors cannot adequately accommodate differing object scales, varying point-cloud density, part-deformation and clutter. We present a proposal refinement module inspired by 2D deformable convolution networks that can adaptively gather instance-specific features from locations where informative content exists. We also propose a simple context gating mechanism which allows the keypoints to select relevant context information for the refinement stage. We show state-of-the-art results on the KITTI dataset.
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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 Moderate val | Deformable PV-RCNN | AP | 83.3 | #5 of 11 | Archive leaderboard | report |
| 3D Object Detection | KITTI Cyclists Moderate val | Deformable PV-RCNN | AP | 73.46 | #1 of 1 | Archive leaderboard | report |
| 3D Object Detection | KITTI Pedestrians Moderate val | Deformable PV-RCNN | AP | 58.33 | #1 of 1 | 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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