3D shape retrieval by visual parts similarity.

In this paper we propose a novel algorithm for 3D shape searching based on the visual similarity by cutting the object into parts. This method rectify some of the shortcomings of the visual similarity based methods, so that it can better account for objects with deformation, articulation, concave areas, and parts of the object not visible because of self occlusion. As the first step, the 3D objects are partitioned into a number of parts by using cutting planes or by mesh segmentation. Then a number of silhouettes from different directions are rendered of those parts. Then Zernike moments are applied on the silhouettes to generate shape descriptors. The distance measure is based on minimizing the distance among all the combinations of shape descriptors and then these distances are used for similarity based searching.