Content-Based Retrieval of 3D Non Rigid Object with Sparse Representations and Local Feature Descriptors: A Comparative Study

Content-based 3D shape retrieval (CBR) become a central research topic in the field of pattern recognition. The primary challenge for CBR methods is to extract proper features for discriminating diverse shapes of 3D models with non-rigid deformations. The bag-of-features (BoF) paradigm and Local Feature Descriptors (LFDs) have demonstrated impressive levels of performance. The success of this approach prompted several extensions to BoF, such as sparse coding approach. In this study, state-of-the-art content-based 3D shape retrieval methods based on local features and sparse coding are investigated in detail. Performance of the well-known local spectral descriptors (GPS, HKS, SIHKS, WKS and meshSIFT) is tested with several algorithms for dictionaries learning and coefficient learning. Experiments are performed on the standard dataset Shrec11 with different scenarios.