Recently, many deep neural networks were designed to process 3D point clouds,
but a common drawback is that rotation invariance is not ensured, leading to
poor generalization to arbitrary orientations. In this paper, we introduce a
new low-level purely rotation-invariant representation to replace common 3D
Cartesian coordinates as the network inputs. Also, we present a network
architecture to embed these representations into features, encoding local
relations between points and their neighbors, and the global shape structure.
To alleviate inevitable global information loss caused by the
rotation-invariant representations, we further introduce a region relation
convolution to encode local and non-local information. We evaluate our method
on multiple point cloud analysis tasks, including shape classification, part
segmentation, and shape retrieval. Experimental results show that our method
achieves consistent, and also the best performance, on inputs at arbitrary
orientations, compared with the state-of-the-arts.

本文提出了一种新的低层纯旋转不变表示，以替代常见的 3D 笛卡尔坐标作为神经网络的输入，并介绍了一种网络结构来将这些表示嵌入为特征，编码点与邻居之间以及全局形状结构之间的局部关系，并通过区域关系卷积来编码局部和非局部信息以缓解因旋转不变表示引起的全局信息丢失。在多个点云分析任务上评估我们的方法，包括形状分类、部件分割和形状检索，实验结果表明与现有技术相比，我们的方法在任意方向上的输入上实现了一致且最佳的性能。