SORCNet: robust non-rigid shape correspondence with enhanced descriptors by Shared Optimized Res-CapsuleNet

Yuanfeng Lian, Dingru Gu, Jing Hua

Abstract: 3D non-rigid shape correspondence, as an important research topic in 3D shape analysis, is useful but challenging in computer graphics, computer vision, and pattern recognition. Despite recent success of several deep neural networks for shape correspondence, those networks cannot achieve robust results on non-rigid objects due to their local deformation complexity. This paper presents a novel and efficient shape correspondence network—Shared Optimized Res-CapsuleNet (SORCNet)—that learns point features based on enhanced descriptors to solve dense correspondence between non-rigid 3D shapes. To further improve the iterative efficiency and accuracy of the model, we design an optimized residual network structure, based on the stochastic gradient descent algorithm with momentum and weight decay (SGDW). Moreover, as the convolutional neural network does not perform well when the shape has directional variance, we present a shared capsule network structure with dual routings, which correlates the hierarchical geometric relationships of the semantic parts well to extract more informative point features. We proved that the primary capsule has a greater influence on feature extraction than the routing and decoder parts. The entire network, SORCNet, is integrated and trained/tested by taking the descriptors and Laplacian eigenbases of two shapes as input. The experiments on public datasets, such as FAUST, SCAPE, TOSCA and KIDS, demonstrate the better effectiveness, accuracy, and adaptability of our method than those of the state of the art in 3D shape correspondence.