HRGE Net Hierarchical Relational Graph Embedding Network for Multiview 3 D Shape Recognition
View based approach that recognizes 3D shape through its projected 2D images achieved state of the art performance for 3D shape recognition. One essential challenge for view based approach is how to aggregate the multi view features extracted from 2D images to be a global 3D shape descriptor. In this work, we propose a novel feature aggregation network by fully investigating the relations among views. We construct a relational graph with multi view images as nodes, and design relational graph embedding by modeling pairwise and neighboring relations among views. By gradually coarsening the graph, we build a hierarchical relational graph embedding network (HRGE Net) to aggregate the multi view features to be a global shape descriptor. Extensive experiments show that HRGE Net achieves stateof the art performance for 3D shape classification and retrieval on benchmark datasets.