Deep similarity network fusion for 3D shape classification

In this paper, we introduce a deep similarity network fusion framework for 3D shape classification using a graph convolutional neural network, which is an efficient and scalable deep learning model for graph-structured data. The proposed approach coalesces the geometrical discriminative power of geodesic moments and similarity network fusion in an effort to design a simple, yet discriminative shape descriptor. This geometric shape descriptor is then fed into a graph convolutional neural network to learn a deep feature representation of a 3D shape. We validate the predictive power of our method on ModelNet shape benchmarks, demonstrating that the proposed framework yields significant performance gains compared to state-of-the-art approaches.