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The geometric and semantic information of 3D point clouds significantly influence the analysis of 3D point cloud structures. However, semantic learning of 3D point clouds based on deep learning is challenging due to the naturally unordered data structure. In this work, we strive to impart machines with the knowledge of 3D object shapes, thereby enabling them to infer the high-level semantic information from the 3D model. Inspired by the vector of locally aggregated descriptors, we propose indirectly describing the high-level semantic information by associating each point’s low-level geometric descriptor with a few visual words. Based on this approach, we design an end-to-end network for 3D shape analysis that combines pointwise low-level geometric and high-level semantic information. The network includes a spatial transform and a uniform operation that make it invariant to input rotation and translation …