Multi-Range View Aggregation Network With Vision Transformer Feature Fusion for 3D Object Retrieval

View-based methods have achieved state-of-the-art performance in 3D object retrieval. However, view-based methods still encounter two major challenges. The first is how to leverage the inter-view correlation to enhance view-level visual features. The second is how to effectively fuse view-level features into a discriminative global descriptor. Towards these two challenges, we propose a multi-range view aggregation network (MRVA-Net) with a vision transformer based feature fusion scheme for 3D object retrieval. Unlike the existing methods which only consider aggregating neighboring or adjacent views which could bring in redundant information, we propose a multi-range view aggregation module to enhance individual view representations through view aggregation beyond only neighboring views but also incorporate the views at different ranges. Furthermore, to generate the global descriptor from view-level features, we propose to employ the multi-head self-attention mechanism introduced by vision transformer to fuse the view-level features. Extensive experiments conducted on three public datasets including ModelNet40, ShapeNet Core55 and MCB-A demonstrate the superiority of the proposed network over the state-of-the-art methods in 3D object retrieval.