Enhancing the Local Graph Semantic Feature for 3D Point Cloud Classification and Segmentation

Most graph convolutional neural networks process point clouds by constructing local graphs, then increasing the number of channels by $1\times 1$ convolution, and using max pooling aggregation features. However, there is no direct semantic information interaction between channels after aggregating features. Moreover, only max pooling is used after graph construction, which loses the most features of points. Therefore, we propose a new method to enhance the local graph semantic feature (EDGS) of point cloud. This method consists of semantic feature interaction branch, graph attention branch, and feature aggregation. We use $k$ -nearest neighbor algorithm to construct local graphs followed by multi-layer perceptron. After that, we use two branches to extract local features. In the first branch, max pooling is used to aggregate local graphs semantic features. Then, the concept of grouping is adopted to guide the semantics of individual channels by using the semantics of group channels with feature interaction. In the second branch, in order to preserve the features of points, we use graph attention to assign different weights on the local graph, and sum to aggregate the features between points. Finally, two learnable parameters are used to adaptively aggregate the local features of the two branches. Experimental results show that this method improves the performance on ModelNet40 and ShapeNetPart datasets. The results are 93.5% and 85.6% respectively.