Noise Smoothing using the 2D/3D Magnitude Ratio of Mesh Data

메쉬 데이터의 2D/3D 면적비를 이용한 잡음 평활화

  • Published : 2009.04.30

Abstract

Reconstructed 3D data from computer vision includes necessarily a noise or an error. When these data goes through a mesh process, the different 3D mesh data from original shape comes to make by a noise or an error. This paper proposed the method that smooths a noise effectively by noise analysis in reconstructed 3D data. Because the proposed method is smooths a noise using the area ratio of the mesh, the pre-processing of unusable mesh is necessary in 3D mesh data. This study detects a peak noise and Gaussian noise using the ratio of 3D volume and 2D area of mesh and smooths the noise with respect of its characteristics. The experimental results using synthetic and real data demonstrated the efficacy and performance of proposed algorithm.

컴퓨터 비전을 이용하여 3D로 재구성된 데이터는 필연적으로 잡음이나 에러를 포함하게 된다. 이런 데이터를 메쉬화하면 잡음이나 에러로 인해 본래 물체와 다른 3차원 메쉬 데이터가 만들어진다. 본 논문은 3차원 복원으로 재구성된 3차원 메쉬 데이터에서 잡음을 효과적으로 평활화하는 방법을 제안한다. 제안된 방법은 메쉬의 2/3차원 면적 크기의 비를 이용하여 잡음을 평활화하기 때문에 면적이 큰 3차원 메쉬 데이터에 대한 사전처리가 필요하다. 메쉬의 3차원 면적과 투영된 2차원 면적의 비를 이용해서 3차원 메쉬 데이터에 존재하는 피크 잡음 가우시안 잡음을 검출하고, 잡음의 특성에 따라 이들을 평활화한다. 컴퓨터로 만들어진 3D 데이터와 컴퓨터 비젼 방법으로 얻어진 실제 3D 데이터를 사용한 실험 결과가 제안된 알고리즘의 효율성과 성능을 증명한다.

Keywords

References

  1. Marc Pollefeys, "Tutorial on 3D modeling from images," Dublin, Ireland In conjunction with ECCV, Lecture Notes, CH. 6, 26 June 2000.
  2. Zhengyou ZHANG, "A Robust Technique for Matching Two Uncalibrated Images Through the Recovery of the Unknown Epipolar Geometry," Technical Report, May, 1994.
  3. A. Heyden and K. Astrom, "Euclidean Reconstruction from Constant Intrinsic Parameters," Proc. 13th International Conference on Pattern Recognition, pp. 339-343, 1996.
  4. R. Hartley, J.L, Mundy, A. Zisserman, and D. Forsyth, "Euclidean reconstruction from uncalibrated views," Applications of Invariance in Computer Vision, Lecture Notes in Computer Science, Vol.825, pp, 237-256, 1994.
  5. H.S. Sawhney, Y. Guo, J. Asmuth, and R. Kumar, "Multi-View 3D Estimation and Applications to Match Move," In Proc. IEEE MVIEW, pp. 21-28, 1999.
  6. R. Klette and P. Zamperori, "Handbook of Image Proceedings Operators," John Wileys & Sons, pp. 78-85, 2001.
  7. D.Field, "Laplacian smoothing and Delaunay triangulations,"Communications in Applied Numerical Methods, pp. 709-712, 1988.
  8. Hirokazu Yagou, Alexander Belyaevy, and Daming Weiz, "Mesh Median Filter for Smoothing 3-D Polygonal Surfaces," Proc. of the First International Symposium on Cyber Worlds, pp. 145-150, 2002.
  9. Taubin, G. "A signal processing approach to fair surface design," Proc. of the 22nd annual conference on Computer graphics and interactive techniques, pp. 351-358, 1995.
  10. L. Kobbelt, S. Campagna, J. Vorsatz, and H.P. Seidel. "Interactive multiresolution modeling on arbitrary meshes," In Computer Graphics (SIGGRAPH Proc.), pp. 105-114, 1998.
  11. Desbrun, M., Meyer, M., Schroder, P., and Barr, A. H., "Implicit fairing of irregular meshes using diffusion and curvature flow," The SIGGRAPH Proc., pp. 317-324, 1999.
  12. T. Mashiko, H.Yagou, D. Wei, Y. Ding, and G. Wu, "3D Triangle Mesh Smoothing via Adaptive MMSE Filtering," International Conference on Computer and Information Technology, pp. 201-208, 2004.
  13. Tang Jie and Zhang Fuyan, "Anisotropic Feature-Preserving Smoothing of 3D Mesh," Proc. of the Computer Graphics, Imaging and Vision, pp. 135-141, 2005.
  14. Yutaka Ohtake, Alexander Belyaev, and Ilia Bogaevski, "Mesh regularization and adaptive smoothing," Computer-Aided Design, Vol. 33, pp. 789-800, 2001. https://doi.org/10.1016/S0010-4485(01)00095-1
  15. Tasdizen, Whitaker, Burchard, and Osher, "Geometric surface smoothing via anisotropic diffusion of normals," Proc. of the conference on Visualization, pp. 125-132, 2002.
  16. Yuanfeng Zhou, Caiming Zhang, and Shanshan Gao, "A Quasi-Laplacian Smoothing Approach on Arbitrary Triangular Meshes," IEEE International Conference on Computer-Aided Design and Computer Graphics, pp. 282-287, 2007.