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View-based approach that recognizes 3D shape through its projected 2D imagesachieved state-of-the-art performance for 3D shape recognition. One essentialchallenge for view-based approach is how to aggregate the multi-view featuresextracted from 2D images to be a global 3D shape descriptor. In this work, wepropose a novel feature aggregation network by fully investigating therelations among views. We construct a relational graph with multi-view imagesas nodes, and design relational graph embedding by modeling pairwise andneighboring relations among views. By gradually coarsening the graph, we builda hierarchical relational graph embedding network (HRGE-Net) to aggregate themulti-view features to be a global shape descriptor. Extensive experiments showthat HRGE-Net achieves stateof-the-art performance for 3D shape classificationand retrieval on benchmark datasets.

HRGE-Net: Hierarchical Relational Graph Embedding Network for Multi-view 3D Shape Recognition