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