  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.
