Learning intra-region contexts and inter-region relations are two effective
strategies to strengthen feature representations for point cloud analysis.
However, unifying the two strategies for point cloud representation is not
fully emphasized in existing methods. To this end, we propose a novel framework
named Point Relation-Aware Network (PRA-Net), which is composed of an
Intra-region Structure Learning (ISL) module and an Inter-region Relation
Learning (IRL) module. The ISL module can dynamically integrate the local
structural information into the point features, while the IRL module captures
inter-region relations adaptively and efficiently via a differentiable region
partition scheme and a representative point-based strategy. Extensive
experiments on several 3D benchmarks covering shape classification, keypoint
estimation, and part segmentation have verified the effectiveness and the
generalization ability of PRA-Net. Code will be available at
this https URL .

提出了一种名为 PRA-Net 的新型架构，包含 Intra-region Structure Learning（ISL）模块和 Inter-region Relation Learning（IRL）模块，用于强化点云分析的特征表示，既可以动态集成局部结构信息，也可以通过不同 iable 区域划分方案和代表点策略高效地自适应地捕获区域间关系。