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Hierarchical Surface Reconstruction from Multi-resolution Point Samples

  • Ronny Klowsky
  • Patrick Mücke
  • Michael Goesele
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7474)

Abstract

Robust surface reconstruction from sample points is a challenging problem, especially for real-world input data. We present a new hierarchical surface reconstruction based on volumetric graph-cuts that incorporates significant improvements over existing methods. One key aspect of our method is, that we exploit the footprint information which is inherent to each sample point and describes the underlying surface region represented by that sample. We interpret each sample as a vote for a region in space where the size of the region depends on the footprint size. In our method, sample points with large footprints do not destroy the fine detail captured by sample points with small footprints. The footprints also steer the inhomogeneous volumetric resolution used locally in order to capture fine detail even in large-scale scenes. Similar to other methods our algorithm initially creates a crust around the unknown surface. We propose a crust computation capable of handling data from objects that were only partially sampled, a common case for data generated by multi-view stereo algorithms. Finally, we show the effectiveness of our method on challenging outdoor data sets with samples spanning orders of magnitude in scale.

Keywords

Surface Reconstruction Footprint Size Octree Level Surface Reconstruction Algorithm Hierarchical Surface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Alliez, P., Cohen-Steiner, D., Tong, Y., Desbrun, M.: Voronoi-based variational reconstruction of unoriented point sets. In: Proc. of Eurographics Symposium on Geometry Processing (2007)Google Scholar
  2. 2.
    Boykov, Y., Kolmogorov, V.: Computing geodesics and minimal surfaces via graph cuts. In: Proc. of IEEE International Conference on Computer Vision (2003)Google Scholar
  3. 3.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence (2004)Google Scholar
  4. 4.
    Boykov, Y., Veksler, O.: Graph cuts in vision and graphics: Theories and applications. In: Handbook of Mathematical Models in Computer Vision (2006)Google Scholar
  5. 5.
    Curless, B., Levoy, M.: A volumetric method for building complex models from range images. In: Proc. of ACM SIGGRAPH (1996)Google Scholar
  6. 6.
    Fuhrmann, S., Goesele, M.: Fusion of depth maps with multiple scales. In: Proc. of ACM SIGGRAPH Asia (2011)Google Scholar
  7. 7.
    Furukawa, Y., Curless, B., Seitz, S.M., Szeliski, R.: Towards internet-scale multi-view stereo. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition (2010)Google Scholar
  8. 8.
    Goesele, M., Snavely, N., Curless, B., Hoppe, H., Seitz, S.M.: Multi-view stereo for community photo collections. In: Proc. of IEEE International Conference on Computer Vision (2007)Google Scholar
  9. 9.
    Habbecke, M., Kobbelt, L.: A surface-growing approach to multi-view stereo reconstruction. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition (2007)Google Scholar
  10. 10.
    Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W.: Surface reconstruction from unorganized points. In: Proc. of ACM SIGGRAPH (1992)Google Scholar
  11. 11.
    Hornung, A., Kobbelt, L.: Hierarchical volumetric multi-view stereo reconstruction of manifold surfaces based on dual graph embedding. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition (2006)Google Scholar
  12. 12.
    Hornung, A., Kobbelt, L.: Robust reconstruction of watertight 3D models from non-uniformly sampled point clouds without normal information. In: Proc. of Eurographics Symposium on Geometry Processing (2006)Google Scholar
  13. 13.
    Kazhdan, M., Bolitho, M., Hoppe, H.: Poisson surface reconstruction. In: Proc. of Eurographics Symposium on Geometry Processing (2006)Google Scholar
  14. 14.
    Labatut, P., Pons, J.P., Keriven, R.: Robust and efficient surface reconstruction from range data. Computer Graphics Forum (2009)Google Scholar
  15. 15.
    Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3D surface construction algorithm. In: Proc. of ACM SIGGRAPH (1987)Google Scholar
  16. 16.
    Manson, J., Schaefer, S.: Isosurfaces over simplicial partitions of multiresolution grids. In: Proc. of Eurographics (2010)Google Scholar
  17. 17.
    Middlebury multi-view stereo evaluation, http://vision.middlebury.edu/mview/
  18. 18.
    Mücke, P., Klowsky, R., Goesele, M.: Surface reconstruction from multi-resolution sample points. In: Proc. of Vision, Modeling and Visualization (2011)Google Scholar
  19. 19.
  20. 20.
    Seitz, S.M., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition (2006)Google Scholar
  21. 21.
    Shalom, S., Shamir, A., Zhang, H., Cohen-Or, D.: Cone carving for surface reconstruction. In: Proc. of ACM SIGGRAPH Asia (2010)Google Scholar
  22. 22.
    Sinha, S.N., Mordohai, P., Pollefeys, M.: Multi-view stereo via graph cuts on the dual of an adaptive tetrahedral mesh. In: Proc. of IEEE International Conference on Computer Vision (2007)Google Scholar
  23. 23.
    Snavely, N., Seitz, S.M., Szeliski, R.: Skeletal sets for efficient structure from motion. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition (2008)Google Scholar
  24. 24.
    Vu, H.H., Keriven, R., Labatut, P., Pons, J.P.: Towards high-resolution large-scale multi-view stereo. In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition (2009)Google Scholar
  25. 25.
    Zach, C., Pock, T., Bischof, H.: A globally optimal algorithm for robust TV-L1 range image integration. In: Proc. of IEEE International Conference on Computer Vision (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ronny Klowsky
    • 1
  • Patrick Mücke
    • 1
  • Michael Goesele
    • 1
  1. 1.TU DarmstadtGermany

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