Various recent methods attempt to implement rotation-invariant 3D deep learning by replacing the input coordinates of points with relative distances and angles. Due to the incompleteness of these low-level features, they have to undertake the expense of losing global information. In this paper, we propose the CRIN, namely Centrifugal Rotation-Invariant Network. CRIN directly takes the coordinates of points as input and transforms local points into rotation-invariant representations via centrifugal reference frames. Aided by centrifugal reference frames, each point corresponds to a discrete rotation so that the information of rotations can be implicitly stored in point features. Unfortunately, discrete points are far from describing the whole rotation space. We further introduce a continuous distribution for 3D rotations based on points. Furthermore, we propose an attention-based down-sampling strategy to sample points invariant to rotations. A relation module is adopted at last for reinforcing the long-range dependencies between sampled points and predicts the anchor point for unsupervised rotation estimation. Extensive experiments show that our method achieves rotation invariance, accurately estimates the object rotation, and obtains state-of-the-art results on rotation-augmented classification and part segmentation. Ablation studies validate the effectiveness of the network design.

本篇论文提出了一种基于离心参考帧的旋转不变网络，将点云坐标作为输入，并通过离心参考帧将局部点变换为旋转不变表示，再基于离散点提出了连续的三维旋转分布，并引入了基于注意力机制的下采样策略，通过学习长距离依赖关系进而实现无监督旋转估计，在旋转不变、对象旋转估计以及旋转增强分类和部分划分等方面取得了最先进的结果。

CRIN：基于离心参考系的旋转不变点云分析与旋转估计