Improved Three-Dimensional Model Feature of Non-rigid Based on HKS

Abstract

The recognition and retrieval of 3D models have been a hot spot in the field of computer vision. Since the non-rigid shapes can generate various deformations, the recognition and retrieval of non-rigid 3D models are more complex and challenging than rigid one. Therefore, the key to the recognition and retrieval of non-rigid 3D models is to extract a feature which obtains substantial description ability and stability. An improved HKS feature named NSIHKS (NSIHKS, new scale Invariance heat kernel signature) was used to describe the shape of models in the paper. NSIHKS contains intrinsic invariance, scale transformation invariance, robustness et al. Moreover it has good resistance even under faint noise. Firstly, the NSIHKS features of each model were extracted and processed with clustering algorithm. Secondly, an efficient algorithm of similarity measurement was designed on the basis of Ming distance. Finally, NSIHKS features of each model in the standard data set were compared via the aforementioned distance algorithm. Experimental results of standard data set in this field show that this feature has good effect on the application of non-rigid 3D model retrieval.