Open AccessPose-invariant matching for non-rigid 3D models using Isomap
Author(s) -
Hairong Jin,
Haichao Huang,
Zhiqiang Wang,
Yuqing Xie,
Xinyue Zhou,
Liming Huang,
Zhouzhenyan Hong
Publication year - 2022
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0264192
Subject(s) - isomap , invariant (physics) , pattern recognition (psychology) , scaling , artificial intelligence , computer science , polygon mesh , matching (statistics) , feature matching , algorithm , mathematics , dimensionality reduction , feature extraction , nonlinear dimensionality reduction , geometry , statistics , mathematical physics , computer graphics (images)
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.