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.
翻译:在实际应用中,没有前缀的点云分析非常具有挑战性,因为点云的方向往往不为人知。在本文中,我们提议了一个全新的点定学习框架PRIN,即点旋转-变量网络,重点是在点云分析中旋转-变量特性提取。我们用Density Indeptive Smalling构建球形信号,以应对球空间扭曲的点分布。此外,我们提议球形Voxel变异和点再取样,以提取每个点的旋转-变量特征。我们的网络可以应用到对象分类、部分分割、3D特征匹配和标签协调等任务。我们显示,在随机旋转点云的数据集上,PRIN的表现优于没有数据增强的状态-艺术方法。我们还为我们的方法实现的旋转-变量提供了理论分析。