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

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

Volume: 14 Issue: 8

Author(s): Dechao Sun, Nenglun Chen*, Renfang Wang, Bangquan Liu and Feng Liang*

Affiliation:

• Department of Computer Science, Hong Kong University, Hongkong,China

• College of Big Data and Software Engineering, Zhejiang Wanli University, Ningbo,China

Keywords: Salient feature points, 3D models, decision graph, geodesic distance, isometric transformation, Heat ConductionEquation, clustering algorithm, Singular point.

Abstract: 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.

Method: 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.

Cite this article as:

Sun Dechao , Chen Nenglun *, Wang Renfang , Liu Bangquan and Liang Feng *, Computing Salient Feature Points of 3D Model Based on Geodesic Distance and Decision Graph Clustering, Recent Advances in Computer Science and Communications 2021; 14 (8) . https://dx.doi.org/10.2174/2666255813999200928215032

DOI https://dx.doi.org/10.2174/2666255813999200928215032	Print ISSN 2666-2558
Publisher Name Bentham Science Publisher	Online ISSN 2666-2566