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Point clouds are unstructured and unordered in the embedded 3D space. Inorder to produce consistent responses under different permutation layouts, mostexisting methods aggregate local spatial points through maximum or summationoperation. But such an aggregation essentially belongs to the isotropicfiltering on all operated points therein, which tends to lose the informationof geometric structures. In this paper, we propose a spatial transformer pointconvolution (STPC) method to achieve anisotropic convolution filtering on pointclouds. To capture and represent implicit geometric structures, we specificallyintroduce spatial direction dictionary to learn those latent geometriccomponents. To better encode unordered neighbor points, we design sparsedeformer to transform them into the canonical ordered dictionary space by usingdirection dictionary learning. In the transformed space, the standardimage-like convolution can be leveraged to generate anisotropic filtering,which is more robust to express those finer variances of local regions.Dictionary learning and encoding processes are encapsulated into a networkmodule and jointly learnt in an end-to-end manner. Extensive experiments onseveral public datasets (including S3DIS, Semantic3D, SemanticKITTI)demonstrate the effectiveness of our proposed method in point clouds semanticsegmentation task.

Spatial Transformer Point Convolution