Abstract: 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.

Key words: point cloud classification and segmentation, encoding unit, attention mechanism, multidimensional loss function