{
  "id": "2011.12149",
  "version": "v1",
  "published": "2020-11-24T15:00:56.000Z",
  "updated": "2020-11-24T15:00:56.000Z",
  "title": "SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration",
  "authors": [
    "Sheng Ao",
    "Qingyong Hu",
    "Bo Yang",
    "Andrew Markham",
    "Yulan Guo"
  ],
  "categories": [
    "cs.CV",
    "cs.AI",
    "cs.LG",
    "cs.RO"
  ],
  "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2020-11-24T15:00:56.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "3d point cloud registration",
      "general surface descriptor",
      "cylindrical convolutional neural layers",
      "outperforms existing state-of-the-art techniques",
      "invariant whilst sufficiently informative"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}