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In point cloud generation and completion, previous methods for transforminglatent features to point clouds are generally based on fully connected layers(FC-based) or folding operations (Folding-based). However, point cloudsgenerated by FC-based methods are usually troubled by outliers and roughsurfaces. For folding-based methods, their data flow is large, convergencespeed is slow, and they are also hard to handle the generation of non-smoothsurfaces. In this work, we propose AXform, an attention-based method totransform latent features to point clouds. AXform first generates points in aninterim space, using a fully connected layer. These interim points are thenaggregated to generate the target point cloud. AXform takes both parametersharing and data flow into account, which makes it has fewer outliers, fewernetwork parameters, and a faster convergence speed. The points generated byAXform do not have the strong 2-manifold constraint, which improves thegeneration of non-smooth surfaces. When AXform is expanded to multiple branchesfor local generations, the centripetal constraint makes it has properties ofself-clustering and space consistency, which further enables unsupervisedsemantic segmentation. We also adopt this scheme and design AXformNet for pointcloud completion. Considerable experiments on different datasets show that ourmethods achieve state-of-the-art results.

Attention-based Transformation from Latent Features to Point Clouds