No abstract

Deformable non-rigid 3D shape retrieval plays an important role in various applications. Although there are many related works, their precision and robustness are not ideal. In this paper, we develop a novel retrieval method by using graph contexts, which consists of three steps. Initially, we evaluate the performance of spectral distances for deformable shape representation, which has not been studied in detail before. Then, we create a weighted L2 distance for similarity measurement based on the spectra of Laplace-Beltrami operator. Finally, a new local graph diffusion method is introduced to reduce the mismatch error in feature space and the time cost of diffusion has reduced a lot simultaneously. Our experiment results on SHREC'11 Non-rigid dataset have reached the best reported retrieval performance (MAP: 99.9%).

Improving query quality and robustness is a hot topic in information and image retrieval field, which has resulted in many interesting works. To address the same problem for deformable non-rigid 3D shape retrieval, two topics are considered in this paper. The first one we discussed is shape representation, which is related to feature extraction and fusion. For feature extraction, we create a global feature to achieve a coarser-scale shape appearance description. Then, to alleviate the drawbacks of retrieval by single feature, we develop a novel fusion method for multiple feature fusion, which turns out to be superior to weighted sum approach with a low complexity. The second topic studied in this paper is to further refine the retrieval results by introducing a new retrieval guidance algorithm based on category prediction. To evaluate the proposed methods, experiments on three popular nonrigid datasets are carried out. The evaluation results suggest that our shape representation method has achieved state-of-the-art performance. Then, by adjusting the retrieval results of existing methods, our retrieval guidance algorithm has promoted the accuracy with nice effects.