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Directly learning features from the point cloud has become an active researchdirection in 3D understanding. Existing learning-based methods usuallyconstruct local regions from the point cloud and extract the correspondingfeatures. However, most of these processes do not adequately take the spatialdistribution of the point cloud into account, limiting the ability to perceivefine-grained patterns. We design a novel Local Spatial Aware (LSA) layer, whichcan learn to generate Spatial Distribution Weights (SDWs) hierarchically basedon the spatial relationship in local region for spatial independent operations,to establish the relationship between these operations and spatialdistribution, thus capturing the local geometric structure sensitively.Wefurther propose the LSANet, which is based on LSA layer, aggregating thespatial information with associated features in each layer of the networkbetter in network design.The experiments show that our LSANet can achieve onpar or better performance than the state-of-the-art methods when evaluating onthe challenging benchmark datasets. For example, our LSANet can achieve 93.2%accuracy on ModelNet40 dataset using only 1024 points, significantly higherthan other methods under the same conditions. The source code is available athttps://github.com/LinZhuoChen/LSANet.

LSANet: Feature Learning on Point Sets by Local Spatial Aware Layer