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.

翻译：在本文中,我们引入了一个新的、但概念上简单的神经结构,称为SpinNet,以提取本地特征,这些特征在概念上是反复变化的,同时有足够的信息来进行准确的登记。首先引入了一个空间点变换器,将输入的本地表面映射成一个精心设计的圆柱形空间,从而能够以SO(2)等变量代表形式实现端到端优化。一个神经特征提取器,利用强大的点基和3D圆柱形神经层来生成一个紧凑和有代表性的缩写符,用于匹配。在室内和室外数据集上进行的广泛实验表明,SpinNet大大超越了现有状态技术。更为关键的是,它拥有以不同传感器模式在各种不可见情景中的最佳一般化能力。该代码可在 https://github.com/QingyongHu/QingHingyong/QingHinghu上查阅。