Learning Local Structure Of Representative Points For Point Cloud Classification And Semantic Segmentation

Directly processing large point clouds is inefficient. State-of-the-art frameworks hierarchically employ Farthest Point Sampling (FPS) to down-sample the data for point cloud classification, semantic segmentation, etc. However, using only geometric information, FPS neglects the importance of semantic information of each point. To solve this problem, we propose a learning-based block, named Representative Points Block (RPB), to select the most representative points of an irregular point cloud according to the task. RPB takes the information of semantic interest of each point into account, and preserves the structure of the point cloud. We construct our network termed RP-Net by performing feature extraction on hierarchical representative points for point cloud classification and semantic segmentation. With further observation that local shape of representative points is different, a graph-based method is used to explore the features of representative points. Experimental results on challenging benchmarks demonstrate that RPB is more efficient and effective than FPS and RP-Net achieves state-of-the-art performance.