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.

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。

SpinNet：学习一种通用的三维点云表面描述符进行点云配准