Radius-normal histogram and hybrid strategy for 3D shape retrieval

Recent development in computer hardware and modeling technologies has led to a fast increasing of 3D models. To help user find expected models accurately and efficiently, technologies on content-based 3D model retrievals have become one of the most challenging research topic recently. In this paper, radius-normal histogram(RNH) is proposed to describe shape contents and used for shape retrievals. The RNH shape descriptor first uses a series of concentric spheres to capture the point distribution information of the given model. Then for points in each concentric sphere, a radian normal angle is computed to extract the local geometry features. Finally, the radius-normal histogram is constructed by using the extracted shape signatures. The proposed shape representation remains invariant under rotations. It can be generated from the given 3D model efficiently and easily as well. Performance comparisons for the shape benchmark database have proven that the proposed algorithm can achieve better retrieving performance than other similar histogram-based shape representations. To further improve the retrieving performance, different shape features are combined based on dynamic weight selections and a hierarchical architecture is used to speed up the matching process.