2D compressive sensing and multi-feature fusion for effective 3D shape retrieval.

3D shape retrieval is always a challenging research topic because of complex geometric structural variations involved. Although many feature extraction and retrieval algorithms have been proposed, they generally only use single 3D model descriptor hence cannot obtain better retrieval performance. In this paper, we propose a new 3D shape retrieval framework based on compressive sensing (CS) and multi-feature fusion (MFF). Firstly, we extract three new features including the CS Chebyshev ray (CSCR) feature, the CS spatial hierarchical (CSSH) feature and the Extended Gaussian sphere (EGS) feature. Actually, CSCR, CSSH and EGS respectively represent the volume tensor, the layered detail and the statistical space distribution on the model surface of a 3D model. To make the best use of these features, a supervised learning is used to determine the weighting coefficients for these features. Finally, the features and their corresponding weighting coefficients are used to determine the similarity of 3D models in the multi-feature fusion (MFF) framework for 3D shape retrieval. For performance assessment, two publicly available datasets that contain 3D models with large geometric variations are used, including ModelNet-10 and PSB datasets. Comprehensive experimental results have demonstrated the efficacy of the proposed method for 3D shape retrieval.