Attention-Based Local Region Aggregation Network For Hierarchical Point Cloud Learning

In point cloud processing, efficient feature aggregation of local regions is essential for hierarchical representations of 3D shapes. However, most prior works directly aggregate local features by symmetric functions, which will result in the desertion of vital shape information. In this paper, we propose a novel aggregation module based on 3D attention mechanism, named Local Region Attention Aggregation, which can capture the local shape implied in irregular points by emphasizing informative features and suppressing unnecessary ones. Specifically, for each local region, our module first encodes point relations, and then sequentially infers two attention maps generated by 3D geometric relations and fusion of multi-scale features respectively. After that, we multiply encoded point features with the attention maps to refine features adaptively. Consequently, by aggregating the higher-quality local features, shape awareness can be enhanced. Extensive experiments on various challenging benchmarks verify our method achieves state-of-the-art performance.