Multi-Modality Learning for Non-Rigid 3D Shape Retrieval via Structured Sparsity Regularizations

Big challenges are usually occurring in non-rigid 3D shape retrieval, for the shapes undergoing arbitrarily non-affine transformations. In this work, a novel design of feature learning approach is proposed for non-rigid 3D shape retrieval, dubbed Structured Sparsity Regularized Multi-Modality Method (SSR-MM). The shape signatures which capture the deformation-invariant characteristics are averaged and stacked for a multi-modality machine learning approach, and a transform matrix based on the structure sparsity regularization is utilized to map those signatures obtaining the discriminative features for retrieval. The proposed framework is evaluated on the publicly available non-rigid 3D human benchmarks, and the experimental results show the efficacy of our contributions and the advantages of our method over existing ones.

Automated summary

A novel feature learning approach was proposed for non-rigid 3D shape retrieval, which captured deformation-invariant characteristics and mapped them using structured sparsity regularization, showing effectiveness and advantages over existing methods in experiments on public benchmarks.