Pose-invariant matching for non-rigid 3D models using Isomap

• Published in: PloS one Vol. 17; no. 3; p. e0264192
• Main Authors: Jin, Hairong, Huang, Haichao, Wang, Zhiqiang, Xie, Yuqing, Zhou, Xinyue, Huang, Liming, Hong, Zhouzhenyan
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 16.03.2022; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Apexes; Biology and Life Sciences; Computer and Information Sciences; Deep learning; Electric power; Finite element method; Invariants; Methods; Model matching; Modelling; Neural networks; Noise; Physical Sciences; Research and Analysis Methods; Rotation; Similarity measures; Three dimensional models; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0264192

The wide usage of 3D mesh models greatly increases the importance of an effective matching algorithm for them. In this paper, we propose a novel 3D model matching algorithm. Firstly, vertices on the input 3D mesh models are mapped to 1D space by employing Isomap. A pose-invariant feature set is then constructed from the vertices in 1D space. Finally, the similarity between any two 3D models can be computed by comparing their feature sets. Experimental results show that the algorithm is not only invariant to translation, rotation, scaling, but also invariant to different poses of 3D models. Additionally, the algorithm is robust to noise.