Three-dimensional point clouds learning is widely applied, but the point clouds are still unable to deal with classification and recognition tasks satisfactorily in the cases of irregular geometric structures and high-dimensional space. In 3D space, point clouds tend to have regular Euclidean structure because of their density. On the contrary, due to the high dimensionality, the spatial structure of high-dimensional space is more complex, and point clouds are mostly presented in non-European structure. Furthermore, among current 3D point clouds classification algorithms, Canonical Capsules algorithm based on Euclidean distance is difficult to decompose and identify non-Euclidean structures effectively. Thus, aiming at the point clouds classification task of non-Euclidean structure in 3D and high-dimensional space, this paper refers to the LLE algorithm based on geodesic distance for optimizing and proposes the unsupervised algorithm of high-dimensional point clouds capsule. In this paper, the geometric features of point clouds are considered in the extraction process, so as to transform the high-dimensional non-Euclidean structure into a lower-dimensional Euclidean structure with retaining spatial geometric features. To verify the feasibility of the unsupervised algorithm of high-dimensional point clouds capsule, experiments are conducted in Swiss Roll dataset, point clouds MNIST dataset and point clouds LFW dataset. The results show that (1) non-Euclidean structures can be can effectively identified by this model in Swiss Roll dataset; (2) a significant unsupervised learning effect is realized in point clouds MNIST dataset. In conclusion, the high-dimensional point clouds capsule unsupervised algorithm proposed in this paper is conducive to expand the application scenarios of current point clouds classification and recognition tasks.

翻译：3D 空间 3D 空间 3D 空间 3D 空间 3D 空间 3cliidean 结构 常规 Euclidean 结构 。 相反, 高维空间的空间结构比较复杂, 点云大多以非欧洲结构显示。 此外, 在目前的 3D 点云分类算法中, 以 Euclidean 距离为基础的 Canonicial Capsules 算法很难解开和确定非 Euclide 结构。 在 3D 空间 3D 空间 3D 空间 3clidean 结构的点云分类任务中, 3D 和高维空间 空间 结构中, 指基于地深空间空间空间空间空间空间空间空间结构的LLE 算法, 以优化并提出高度云层缩算算法。 在本文中, 以 Euclodiciodeal 数据中, 将高度的非 Euclodeal 值的云流值结构转化为非clodlodal Slodal 。