在点云的处理过程中，许多深度学习网络未能充分考虑局部点之间的复杂关系，导致大量空间几何信息丢失。针对该问题，提出了一个强化局部特征的网络，用于点云的目标分类和语义分割。该网络通过设计编码单元对点的多方向信息进行编码；通过注意力机制学习采样分组后形成局部点云的特征。同时提出了一种新的多维损失函数，结合使用交叉熵损失函数与中心损失函数作用于分类任务。在数据集ModelNet40和ScanNet上进行了大量实验，结果表明：该网络在点云的目标分类和语义分割任务上表现出较好的性能。
In the process of point cloud processing, many deep learning networks fail to fully consider the complicated relationships between local points, resulting in the loss of a large number of spatial geometric information. To solve this problem, an enriching local features network for point cloud classification and segmentation is proposed. The network designs an encoding unit to encode the multi-directional information of points, applies the attention mechanism to learn features of the local points formed after sampling and grouping, and proposes a new multi-dimensional loss function, which combines cross entropy loss function and the center loss function to act on the classification task. Extensive experiments are carried out on Modelnet40 and ScanNet datasets. The experimental results show that the network performs well in object classification and semantic segmentation tasks of 3D point cloud.
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