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
this https URL

本文介绍了一个新的神经架构，称为 SpinNet，旨在提取旋转不变但足够信息丰富以达到精确配准的局部特征。通过空间点变换器和神经特征提取器，可以将输入局部表面映射到经过仔细设计的圆柱空间，并利用点卷积神经层和 3D 圆柱卷积神经层来提取紧凑且代表性的描述符进行匹配。在室内和室外数据集上进行的广泛实验表明，SpinNet 的性能优于现有的最先进技术，并在不同传感器模态下具有最佳的泛化能力。