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Mesh Optimisation Using Edge Information in Feature-Based Surface Reconstruction

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Advances in Visual Computing (ISVC 2006)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 4291))

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Abstract

One of the most challenging and fundamental problems in computer vision is to reconstruct a surface model given a set of uncalibrated 2D images. Well-established Structure from Motion (SfM) algorithms often result in a sparse set of 3D surface points, but surface modelling based on sparse 3D points is not easy. In this paper, we present a new method to refine and optimise surface meshes using edge information in the 2D images. We design a meshing – edge point detection – re-meshing scheme that can gradually refine the surface mesh until it best fits the true physical surface of the object being modelled. Our method is tested on real images and satisfactory results are obtained.

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References

  1. Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W.: Surface reconstruction from unorganized points. In: SIGGRAPH, pp. 71–78 (1992)

    Google Scholar 

  2. Szeliski, R., Tonnesen, D., Terzopoulos, D.: Modeling surfaces of arbitrary topology with dynamic particles. In: CVPR, pp. 82–87 (1993)

    Google Scholar 

  3. Curless, B., Levoy, M.: A volumetric method for building complex models from range images. In: SIGGRAPH, pp. 303–312 (1996)

    Google Scholar 

  4. Zhao, H., Osher, S., Merriman, B., Kang, M.: Implicit and nonparametric shape reconstruction from unorganized data using a variational level set method. CVIU 80(3), 295–314 (2000)

    MATH  Google Scholar 

  5. Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision, 2nd edn. Cambridge University Press, Cambridge (2003)

    Google Scholar 

  6. Triggs, B.: Autocalibration and the absolute quadric. In: CVPR, p. 609 (1997)

    Google Scholar 

  7. Pollefeys, M., Gool, L.V., Vergauwen, M., Verbiest, F., Cornelis, K., Tops, J., Koch, R.: Visual modeling with a hand-held camera. IJCV 59(3), 207–232 (2004)

    Article  Google Scholar 

  8. Lhuillier, M., Quan, L.: Surface reconstruction by integrating 3D and 2D data of multiple views. In: ICCV, vol. 02, p. 1313 (2003)

    Google Scholar 

  9. Lhuillier, M., Quan, L.: A quasi-dense approach to surface reconstruction from uncalibrated images. TPAMI 27(3), 418–433 (2005)

    Google Scholar 

  10. Solem, J.E., Heyden, A.: Reconstructing open surfaces from unorganized data points. In: CVPR, vol. 02, pp. 653–660 (2004)

    Google Scholar 

  11. Paris, S., Sillion, F., Quan, L.: A surface reconstruction method using global graph cut optimization. IJCV 66(2), 141–161 (2006)

    Article  Google Scholar 

  12. Taylor, C.J.: Surface reconstruction from feature based stereo. In: ICCV, vol. 01, p. 184 (2003)

    Google Scholar 

  13. Liu, J., Hubbold, R.: Automatic Camera Calibration and Scene Reconstruction with Scale-Invariant Features. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Nefian, A., Meenakshisundaram, G., Pascucci, V., Zara, J., Molineros, J., Theisel, H., Malzbender, T. (eds.) ISVC 2006. LNCS, vol. 4291, pp. 558–568. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  14. Morris, D., Kanade, T.: Image-consistent surface triangulation. In: CVPR, vol. 1, pp. 332–338 (2000)

    Google Scholar 

  15. Vogiatzis, G., Torr, P., Cipolla, R.: Bayesian stochastic mesh optimisation for 3D reconstruction. In: BMVC, vol. 2, pp. 711–718 (2003)

    Google Scholar 

  16. Nakatuji, A., Sugaya, Y., Kanatani, K.: Mesh optimization using an inconsistency detection template. In: ICCV, pp. 1148–1153 (2005)

    Google Scholar 

  17. Pollefeys, M., Koch, R., Gool, L.V.: Self-calibration and metric reconstruction in spite of varying and unknown internal camera parameters. In: ICCV, p. 90 (1998)

    Google Scholar 

  18. Lowe, D.G.: Object recognition from local scale-invariant features. In: ICCV, p. 1150 (1999)

    Google Scholar 

  19. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. IJCV 60(2), 91–110 (2004)

    Article  Google Scholar 

  20. Canny, J.: A computational approach to edge detection. TPAMI 8, 679–714 (1986)

    Google Scholar 

  21. Goldberger, J., Gordon, S., Greenspan, H.: An efficient image similarity measure based on approximations of KL-divergence between two Gaussian mixtures. In: ICCV, vol. 1, p. 487 (2003)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Manchester, Manchester, M13 9PL, United Kingdom

    Jun Liu & Roger Hubbold

Authors
  1. Jun Liu
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  2. Roger Hubbold
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Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Nevada, Reno, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Digital Image Research Center, Kingston University, London, UK

    Paolo Remagnino

  6. Intel, 95052, Santa Clara, CA, USA

    Ara Nefian

  7. University of California, 430, Computer Science Building, 92697-3425, Irvine, CA, USA

    Gopi Meenakshisundaram

  8. Institute for Data Analysis and Visualization, P.O. Box

    Valerio Pascucci

  9. Department of Computer Science and Engineering, Czech Technical University in Prague, Czech

    Jiri Zara

  10. Rockwell Scientific, 1049 Camino Dos Rios, 91360, Thousand Oaks, CA, USA

    Jose Molineros

  11. Computer Graphics Group, Bielefeld University, D-33501, Bielefeld, Germany

    Holger Theisel

  12. Hewlett Packard Labs, Palo Alto, CA, USA

    Tom Malzbender

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© 2006 Springer-Verlag Berlin Heidelberg

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Liu, J., Hubbold, R. (2006). Mesh Optimisation Using Edge Information in Feature-Based Surface Reconstruction. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2006. Lecture Notes in Computer Science, vol 4291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11919476_44

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  • DOI: https://doi.org/10.1007/11919476_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-48628-2

  • Online ISBN: 978-3-540-48631-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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