Data organization via forming local regions is an integral part of deep learning networks that process 3D point clouds in a hierarchical manner. At each level, the point cloud is sampled to extract representative points and these points are used to be centers of local regions. The organization of local regions is of considerable importance since it determines the location and size of the receptive field at a particular layer of feature aggregation. In this paper, we present two local region-learning modules: Center Shift Module to infer the appropriate shift for each center point, and Radius Update Module to alter the radius of each local region. The parameters of the modules are learned through optimizing the loss associated with the particular task within an end-to-end network. We present alternatives for these modules through various ways of modeling the interactions of the features and locations of 3D points in the point cloud. We integrated both modules independently and together to the PointNet++ object classification architecture, and demonstrated that the modules contributed to a significant increase in classification accuracy for the ScanObjectNN data set.

翻译：通过形成局部区域进行数据组织，是分层处理三维点云的深度学习网络中不可或缺的组成部分。在每个层级中，点云被采样以提取代表性点，这些点被用作局部区域的中心。局部区域的组织方式至关重要，因为它决定了特定特征聚合层中感受野的位置和大小。本文提出两种局部区域学习模块：中心偏移模块用于推断每个中心点的适当偏移量，半径更新模块用于调整每个局部区域的半径。这些模块的参数通过端到端网络中与特定任务相关的损失函数优化进行学习。我们通过多种方式建模三维点云中特征与点位置的相互作用，提出了这些模块的替代方案。我们将这两个模块独立及联合集成到PointNet++物体分类架构中，并证明这些模块显著提升了ScanObjectNN数据集的分类准确率。