General Local Graph Attention in Large-scale Point Cloud Segmentation

Due to the massive number of points and irregular structure, there is challenging to determine local graph relationships in large-scale point clouds. To address this problem, we propose a novel graph attention network that can embed global factors into neighboring points in specific local graphs. In this network, we introduce general local graph, which integrates all points sharing same order positions with encoded central point weights. In other words, it represents the most fundamental features of total local graphs. Besides, local graphs are regenerated to embed global factors by combining general local graph and central point attention. As a result, one point can obtain additional features from corresponding ones with same order positions in different neighborhoods. Experiment on large-scale point cloud segmentation dataset proves our network's competitive performance.