A sparse coding approach for local-to-global 3D shape description

The definition of reliable shape descriptors is an essential topic for 3D object retrieval. In general, two main approaches are considered: global, and local. Global approaches are effective in describing the whole object, while local ones are more suitable to characterize small parts of the shape. Recently some strategies to combine these two approaches have been proposed which are mainly concentrated to the so-called bag of words paradigm. With this paper we address this problem and propose an alternative strategy that goes beyond the bag of word approach. In particular, a sparse coding technique is exploited for the 3D domain: a set of local shape descriptors are collected from the shape, and then a dictionary is trained as generative model. In this fashion the dictionary is used as global shape descriptor for shape retrieval purposes. Several experiments are performed on standard databases in order to evaluate the proposed method in challenging situations like the case of 'SHREC 2011: robustness benchmark' where strong shape transformations are included, and the case of 'SHREC 2007: partial matching track' where composite models are considered in the query phase. A drastic improvement of the proposed method is observed by showing that sparse coding approach is particularly suitable for local-to-global description and outperforms other approaches such as the bag of words.