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

• URL: http://arxiv.org/abs/2011.12149v2
• Date: Fri, 9 Apr 2021 16:42:19 GMT
• Title: SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
• Authors: Sheng Ao, Qingyong Hu, Bo Yang, Andrew Markham, Yulan Guo
• Abstract summary: We introduce a new, yet conceptually simple, neural architecture, termed SpinNet, to extract local features. Experiments on both indoor and outdoor datasets demonstrate that SpinNet outperforms existing state-of-the-art techniques.
• Score: 57.28608414782315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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