Point cloud analysis without pose priors is very challenging in real
applications, as the orientations of point clouds are often unknown. In this
paper, we propose a brand new point-set learning framework PRIN, namely,
Point-wise Rotation Invariant Network, focusing on rotation invariant feature
extraction in point clouds analysis. We construct spherical signals by Density
Aware Adaptive Sampling to deal with distorted point distributions in spherical
space. Spherical Voxel Convolution and Point Re-sampling are proposed to
extract rotation invariant features for each point. In addition, we extend PRIN
to a sparse version called SPRIN, which directly operates on sparse point
clouds. Both PRIN and SPRIN can be applied to tasks ranging from object
classification, part segmentation, to 3D feature matching and label alignment.
Results show that, on the dataset with randomly rotated point clouds, SPRIN
demonstrates better performance than state-of-the-art methods without any data
augmentation. We also provide thorough theoretical proof and analysis for
point-wise rotation invariance achieved by our methods. Our code is available
on this https URL

本文提出了一个全新的点集学习框架 PRIN，即点式旋转不变网络，通过密度感知自适应抽样构建球形信号来解决点云在球形空间上的扭曲分布，运用球形体素卷积和点重采样来提取每个点的旋转不变特征，同时将 PRIN 扩展到一种稀疏版本 SPRIN，直接操作于稀疏点云。两种方法都适用于从对象分类、部分分割到三维特征匹配和标签对齐等任务，结果表明，对于随机旋转点云的数据集，SPRIN 实现了比最先进的方法更好的性能，且无需使用任何数据增强。我们还为方法实现的点式旋转不变性提供了深入的理论证明和分析。

PRIN/SPRIN: 关于提取点旋转不变特征的研究

PRIN/SPRIN: On Extracting Point-wise Rotation Invariant Features

Point cloud analysis without pose priors is very challenging in real
applications, as the orientations of point clouds are often unknown. In this
paper, we propose a brand new point-set learning framework PRIN, namely,
Pointwise Rotation-Invariant Network, focusing on rotation-invariant feature
extraction in point clouds analysis. We construct spherical signals by Density
Aware Adaptive Sampling to deal with distorted point distributions in spherical
space. In addition, we propose Spherical Voxel Convolution and Point
Re-sampling to extract rotation-invariant features for each point. Our network
can be applied to tasks ranging from object classification, part segmentation,
to 3D feature matching and label alignment. We show that, on the dataset with
randomly rotated point clouds, PRIN demonstrates better performance than
state-of-the-art methods without any data augmentation. We also provide
theoretical analysis for the rotation-invariance achieved by our methods.

本文提出了一种点云学习框架 PRIN，其中通过密度感知自适应采样构造球形信号来处理球形空间中的扭曲点分布并利用球形体素卷积和点重新采样来提取每个点的旋转不变特征。该框架在物体分类、部分分割和 3D 特征匹配方面具有可应用性，并且在没有任何数据增强的情况下，在随机旋转的点云数据集上展现出比现有方法更好的性能。同时，本文提供了对通过该方法实现的旋转不变性的理论分析。