Papers › SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
Sheng Ao, Qingyong Hu, Bo Yang, Andrew Markham, Yulan Guo
Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction. Existing learning-based local descriptors are either sensitive to rotation transformations, or rely on classical handcrafted features which are neither general nor representative. In this paper, we introduce a new, yet conceptually simple, neural architecture, termed SpinNet, to extract local features which are rotationally invariant whilst sufficiently informative to enable accurate registration. A Spatial Point Transformer is first introduced to map the input local surface into a carefully designed cylindrical space, enabling end-to-end optimization with SO(2) equivariant representation. A Neural Feature Extractor which leverages the powerful point-based and 3D cylindrical convolutional neural layers is then utilized to derive a compact and representative descriptor for matching. Extensive experiments on both indoor and outdoor datasets demonstrate that SpinNet outperforms existing state-of-the-art techniques by a large margin. More critically, it has the best generalization ability across unseen scenarios with different sensor modalities. The code is available at https://github.com/QingyongHu/SpinNet.
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5eb7bf11c56f4c5f · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Point Cloud Registration | 3DMatch (trained on KITTI) | SpinNet | Recall | 0.845 | #2 of 5 | Archive leaderboard | report |
| Point Cloud Registration | 3DMatch Benchmark | SpinNet (no code published as of Dec 15 2020) | Feature Matching Recall | 97.6 | #4 of 15 | Archive leaderboard | report |
| Point Cloud Registration | ETH (trained on 3DMatch) | SpinNet | Feature Matching Recall | 0.928 | #2 of 20 | Archive leaderboard | report |
| Point Cloud Registration | ETH (trained on 3DMatch) | SpinNet | Recall (30cm, 5 degrees) | 73.07 | #2 of 20 | Archive leaderboard | report |
| Point Cloud Registration | FPv1 | SpinNet | RRE (degrees) | 3.105 | #5 of 8 | Archive leaderboard | report |
| Point Cloud Registration | FPv1 | SpinNet | RTE (cm) | 1.670 | #5 of 8 | Archive leaderboard | report |
| Point Cloud Registration | FPv1 | SpinNet | Recall (3cm, 10 degrees) | 42.46 | #5 of 8 | Archive leaderboard | report |
| Point Cloud Registration | KITTI | SpinNet | Success Rate | 99.10 | #3 of 6 | Archive leaderboard | report |
| Point Cloud Registration | KITTI (trained on 3DMatch) | SpinNet | Success Rate | 81.44 | #9 of 14 | 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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