Papers › SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration

SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration

24 Nov 2020CVPR 2021 1arXiv:2011.12149archive 2025-07-28

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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Tasks

Point Cloud Registration

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

Absolute Position EncodingsAdamAttentionBPEDense ConnectionsDropoutLabel SmoothingLayer NormalizationLinear LayerMulti-Head AttentionPosition-Wise Feed-Forward LayerResidual ConnectionSoftmaxTransformer

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