Pairwise View Weighted Graph Network for View-based 3D Model Retrieval

View-based 3D model retrieval has become an important task in both computer vision and machine learning domains. Although deep learning methods have achieved excellent performances on view-based 3D model retrieval, the intrinsic correlation and the degree of view discrimination among multiple views in a 3D model have not been effectively exploited. To obtain a more efficient feature descriptor for 3D model retrieval, in this work, we propose the pairwise view weighted graph network (abbreviated PVWGN) for view-based 3D model retrieval where non-local graph layers are embedded into the network architecture to automatically mine the intrinsic relationship among multiple views of a 3D model. Furthermore, the view weighted layer is employed in the PVWGN to adaptively assign the weight to each view according to its aggregation information. In addition, the pairwise discrimination loss function is designed to improve the feature discrimination of the 3D model. Most importantly, these three issues are integrated into a unified framework. Extensive experimental results on the ModelNet40 and ModelNet10 3D model retrieval datasets show that PVWGN can outperform all state-of-the-art methods on the 3D model retrieval task with mAPs of 93.2% and 96.2%, respectively.