Computing Salient Feature Points of 3D Model Based on Geodesic Distance and Decision Graph Clustering

摘要

Introduction: Computing Salient Feature Points (SFP) of 3D models has important application value in the field of computer graphics. In order to extract the SFP more effectively,a novel SFP computing algorithm based on geodesic distance and decision graph clustering is proposed. Methods: Firstly,the geodesic distance of model vertices is calculated based on the heat conduction equation,and then the average geodesic distance and importance weight of vertices are calculated. Finally,the decision graph clustering method is used to calculate the decision graph of model vertices. Results and Discussion: 3D models in SHREC 2011 dataset are selected to test the proposed algorithm. Compared with the existing algorithms,this method calculates the SFP of the 3D model from a global perspective. Results show that it is not affected by model posture and noise. Conclusion: Our method maps the SFP of the 3D model to the 2D decision-making diagram,which simplifies the calculation process of SFP,improves the calculation accuracy and possesses strong robustness.