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.

中文翻译：

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SpinNet：学习用于3D点云注册的通用表面描述符

提取鲁棒且通用的3D局部特征是诸如点云注册和重建之类的下游任务的关键。现有的基于学习的局部描述符要么对旋转变换敏感，要么依赖于既不通用也不具有代表性的经典手工特征。在本文中，我们介绍了一种新的概念上简单的神经体系结构，称为SpinNet，以提取旋转不变的局部特征，同时又能提供足够的信息以实现准确的配准。首次引入了Spatial Point Transformer，将输入局部表面映射到精心设计的圆柱空间中，从而可以使用SO（2）等变表示法进行端到端优化。然后利用利用强大的基于点的3D圆柱卷积神经层的神经特征提取器来导出紧凑且具有代表性的描述符以进行匹配。在室内和室外数据集上进行的大量实验表明，SpinNet在很大程度上优于现有的最新技术。更关键的是，它在具有不同传感器模式的未见场景中具有最佳的泛化能力。该代码位于https://github.com/QingyongHu/SpinNet。