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Research in point cloud analysis with deep neural networks has made rapidprogress in recent years. The pioneering work PointNet offered a directanalysis of point clouds. However, due to its architecture PointNet is not ableto capture local structures. To overcome this drawback, the same authors havedeveloped PointNet++ by applying PointNet to local areas. The local areas aredefined by center points and their neighbors. In PointNet++ and its furtherdevelopments the center points are determined with a Farthest Point Sampling(FPS) algorithm. This has the disadvantage that the center points in general donot have meaningful local areas. In this paper, we introduce the neuralLocal-Area-Learning Network (LocAL-Net) which places emphasis on the selectionand characterization of the local areas. Our approach learns critical pointsthat we use as center points. In order to strengthen the recognition of localstructures, the points are given additional metric properties depending on thelocal areas. Finally, we derive and combine two global feature vectors, onefrom the whole point cloud and one from all local areas. Experiments on thedatasets ModelNet10/40 and ShapeNet show that LocAL-Net is competitive for partsegmentation. For classification LocAL-Net outperforms the state-of-the-arts.

Local-Area-Learning Network: Meaningful Local Areas for Efficient Point Cloud Analysis