• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Structure-Preserving Mesh Simplification

Vol. 14, No. 11, November 30, 2020
10.3837/tiis.2020.11.012, Download Paper (Free):


Mesh model generated from 3D reconstruction usually comes with lots of noise, which challenges the performance and robustness of mesh simplification approaches. To overcome this problem, we present a novel method for mesh simplification which could preserve structure and improve the accuracy. Our algorithm considers both the planar structures and linear features. In the preprocessing step, it automatically detects a set of planar structures through an iterative diffusion approach based on Region Seed Growing algorithm; then robust linear features of the mesh model are extracted by exploiting image information and planar structures jointly; finally we simplify the mesh model with plane constraint QEM and linear feature preserving strategies. The proposed method can overcome the known problem that current simplification methods usually degrade the structural characteristics, especially when the decimation is extreme. Our experimental results demonstrate that the proposed method, compared to other simplification algorithms, can effectively improve the quality of mesh and yield an increased robustness on noisy input mesh.


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Cite this article

[IEEE Style]
Z. Chen, X. Zheng, T. Guan, "Structure-Preserving Mesh Simplification," KSII Transactions on Internet and Information Systems, vol. 14, no. 11, pp. 4463-4482, 2020. DOI: 10.3837/tiis.2020.11.012.

[ACM Style]
Zhuo Chen, Xiaobin Zheng, and Tao Guan. 2020. Structure-Preserving Mesh Simplification. KSII Transactions on Internet and Information Systems, 14, 11, (2020), 4463-4482. DOI: 10.3837/tiis.2020.11.012.

[BibTeX Style]
@article{tiis:24035, title="Structure-Preserving Mesh Simplification", author="Zhuo Chen and Xiaobin Zheng and Tao Guan and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2020.11.012}, volume={14}, number={11}, year="2020", month={November}, pages={4463-4482}}