SavedAdd your journal

Journals / Mersin Photogrammetry Journal / 2023 / Cilt: 5 Sayı: 2

Analysing detail preserving capabilities of bilateral, laplacian and taubin mesh filtering methods

Erkan BEŞDOK
Pınar ÇİVİCİOĞLU
Pages
67–74
Publication Date
2023-12-15
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The statistical analysis of the experimental results has shown that the Taubin method is statistically a more successful mesh filtering method for the test sets used in this paper.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Mesh filtering of surfaces is crucial for noise reduction, feature preservation, and mesh simplification in graphics, visualization, and computer vision.
📊
Key Findings & Data:In this paper, the detail preservation capacities of 3 frequently used filters, i.e., Bilateral, Laplacian, and Taubin mesh filters, in mesh filtering have been thoroughly examined by experiments conducted on 4 different test meshes.
💡
Conclusion & Impact:The statistical analysis of the experimental results has shown that the Taubin method is statistically a more successful mesh filtering method for the test sets used in this paper.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Mesh Filtering · Bilateral Filter · Laplacian Filter

Abstract

Mesh filtering of surfaces is crucial for noise reduction, feature preservation, and mesh simplification in graphics, visualization, and computer vision. In this paper, the detail preservation capacities of 3 frequently used filters, i.e., Bilateral, Laplacian, and Taubin mesh filters, in mesh filtering have been thoroughly examined by experiments conducted on 4 different test meshes. While the Bilateral filter excels in preserving sharp features due to its integration of geometric proximity with intensity similarity, the Laplacian filter prioritizes smoothness by averaging neighboring vertex positions, and the Taubin filter offers a balanced approach by merging attributes of both Laplacian and high-pass filters. The Bilateral filter's primary strength lies in its ability to maintain sharp features on a mesh, ensuring that intricate details are preserved by considering both the spatial closeness and intensity similarity of vertices. The Laplacian filter, although effective in achieving mesh smoothness, has the propensity to excessively smooth out sharp and defining features, potentially causing a loss of critical details in the processed mesh. The Taubin filter integrates the best of both worlds, ensuring smoothness without excessive mesh shrinkage; however, it might not excel in feature preservation as effectively as the Bilateral filter or smooth as uniformly as the Laplacian filter, making it a middle-ground option for certain applications. The statistical analysis of the experimental results has shown that the Taubin method is statistically a more successful mesh filtering method for the test sets used in this paper.

🔖

Cite This Article

BEŞDOK, E., ÇİVİCİOĞLU, P. (2023). Analysing detail preserving capabilities of bilateral, laplacian and taubin mesh filtering methods. Mersin Photogrammetry Journal, Cilt: 5 Sayı: 2, 67–74. https://ajindex.com/articles/969560

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (11)

  1. Liu, Y., Coombes, M., & Liu, C. (2023). Mesh-based consensus distributed particle filtering for sensor networks. IEEE Transactions on Signal and Information Processing over Networks, 9, 346-356. https://doi.org/10.1109/TSIPN.2023.3278469
  2. Liu, B., Li, B., Cao, J., Wang, W., & Liu, X. (2023). Adaptive and propagated mesh filtering. Computer-Aided Design, 154, 103422. https://doi.org/10.1016/j.cad.2022.103422 Fábián, G. (2023). Generalized Savitzky–Golay filter for smoothing triangular meshes. Computer Aided Geometric Design, 100, 102167. https://doi.org/10.1016/j.cagd.2022.102167
  3. Han, H. D., & Han, J. K. (2022). Modified bilateral filter for feature enhancement in mesh denoising. IEEE Access, 10, 56845-56862. https://doi.org/10.1109/ACCESS.2022.3176961
  4. Zhong, S., Song, Z., Liu, Z., Xie, Z., Chen, J., Liu, L., & Chen, R. (2021). Shape-aware mesh normal filtering. Computer-Aided Design, 140, 103088. https://doi.org/10.1016/j.cad.2021.103088
  5. Zhao, W., Liu, X., Wang, S., Fan, X., & Zhao, D. (2019). Graph-based feature-preserving mesh normal filtering. IEEE Transactions on Visualization and Computer Graphics, 27(3), 1937-1952. https://do.iorg/10.1109/TVCG.2019.2944357
  6. Zhang, J., Deng, B., Hong, Y., Peng, Y., Qin, W., & Liu, L. (2018). Static/dynamic filtering for mesh geometry. IEEE transactions on visualization and computer graphics, 25(4), 1774-1787. https://do.org/10.1109/TVCG.2018.2816926
  7. Noel, G., Djouani, K., Van Wyk, B., & Hamam, Y. (2012). Bilateral mesh filtering. Pattern Recognition Letters, 33(9), 1101-1107. https://doi.org/10.1016/j.patrec.2012.02.008 Loménie, N., & Stamon, G. (2008). Morphological mesh filtering and α-objects. Pattern Recognition Letters, 29(10), 1571-1579. https://doi.org/10.1016/j.patrec.2008.03.019
  8. Kim, B., & Rossignac, J. (2005). Geofilter: Geometric selection of mesh filter parameters. In Computer Graphics Forum, 24(3), 295-302.
  9. Leipoldt, K. J., Happich, T., Kreysa, E., & Gemünd, H. P. (1991). Scattering matrix methods for far-infrared metal mesh filters. International Journal of Infrared and Millimeter Waves, 12, 263-274. https://doi.org/10.1007/BF01010300
  10. Chen, P. A. (1987). The performance of dielectric coated mesh filter. International Journal of Infrared and Millimeter Waves, 8, 29-33. https://doi.org/10.1007/BF01010643
  11. Byrne, D. M., Brouns, A. J., Case, F. C., Tiberio, R. C., Whitehead, B. L., & Wolf, E. D. (1985). Infrared mesh filters fabricated by electron‐beam lithography. Journal of Vacuum Science & Technology B: Micr