From Multiple Views to Textured 3D Meshes: A GPU-Powered Approach

  • K. Tzevanidis
  • X. Zabulis
  • T. Sarmis
  • P. Koutlemanis
  • N. Kyriazis
  • A. Argyros
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6554)


We present work on exploiting modern graphics hardware towards the real-time production of a textured 3D mesh representation of a scene observed by a multicamera system. The employed computational infrastructure consists of a network of four PC workstations each of which is connected to a pair of cameras. One of the PCs is equipped with a GPU that is used for parallel computations. The result of the processing is a list of texture mapped triangles representing the reconstructed surfaces. In contrast to previous works, the entire processing pipeline (foreground segmentation, 3D reconstruction, 3D mesh computation, 3D mesh smoothing and texture mapping) has been implemented on the GPU. Experimental results demonstrate that an accurate, high resolution, texture-mapped 3D reconstruction of a scene observed by eight cameras is achievable in real time.


Texture Mapping Graphic Hardware Visual Hull Foreground Detection Foreground Segmentation 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kim, H., Sakamoto, R., Kitahara, I., Toriyama, T., Kogure, K.: Compensated Visual Hull with GPU-Based Optimization. In: Huang, Y.-M.R., Xu, C., Cheng, K.-S., Yang, J.-F.K., Swamy, M.N.S., Li, S., Ding, J.-W. (eds.) PCM 2008. LNCS, vol. 5353, pp. 573–582. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Schick, A., Stiefelhagen, R.: Real-Time GPU-Based Voxel Carving with Systematic Occlusion Handling. In: Denzler, J., Notni, G., Süße, H. (eds.) DAGM 2009. LNCS, vol. 5748, pp. 372–381. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  3. 3.
    Matusik, W., Buehler, C., Raskar, R., Gortler, S.J., McMillan, L.: Image-based visual hulls. In: SIGGRAPH 2000: Proceedings of the 27th Annual Conference on Computer Graphics and Interactive Techniques, pp. 369–374. ACM Press/Addison-Wesley Publishing Co., New York, USA (2000)CrossRefGoogle Scholar
  4. 4.
    Matsuyama, T., Wu, X., Takai, T., Nobuhara, S.: Real-time 3D shape reconstruction, dynamic 3D mesh deformation, and high fidelity visualization for 3D video. Computer Vision and Image Understanding 96(3), 393–434 (2004)CrossRefGoogle Scholar
  5. 5.
    Ladikos, A., Benhimane, S., Navab, N.: Efficient visual hull computation for real-time 3D reconstruction using cuda. In: IEEE Conference on Computer Vision and Pattern Recognition, Workshops 2008, pp. 1–8 (2008)Google Scholar
  6. 6.
    Waizenegger, W., Feldmann, I., Eisert, P., Kauff, P.: Parallel high resolution real-time visual hull on gpu. In: IEEE International Conference on Image Processing, pp. 4301–4304 (2009)Google Scholar
  7. 7.
    Sarmis, T., Zabulis, X., Argyros, A.A.: A checkerboard detection utility for intrinsic and extrinsic camera cluster calibration. Technical Report TR-397, FORTH-ICS (2009)Google Scholar
  8. 8.
    Piccardi, M.: Background subtraction techniques: a review. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3099–3104 (2004)Google Scholar
  9. 9.
    Elgammal, A., Harwod, D., Davis, L.: Non-parametric model for background subtraction. In: IEEE International Conference on Computer Vision, Frame-rate Workshop (1999)Google Scholar
  10. 10.
    Han, B., Comaniciu, D., Davis, L.: Sequential kernel density approximation through mode propagation: applications to background modeling. In: Asian Conference on Computer Vision (2004)Google Scholar
  11. 11.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real-time tracking. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 246–252 (1999)Google Scholar
  12. 12.
    Wren, C., Azarbayejani, A., Darrell, T., Pentland, A.: Pfinder: Real-time tracking of the human body. IEEE Transactions on Pattern Analysis and Machine Intelligence 19, 780–785 (1997)CrossRefGoogle Scholar
  13. 13.
    Zivkovic, Z.: Improved adaptive gaussian mixture model for background subtraction. In: International Conference on Pattern Recognition (2004)Google Scholar
  14. 14.
    Prati, A., Mikic, I., Trivedi, M.M., Cucchiara, R.: Detecting moving shadows: Algorithms and evaluation. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 918–923 (2003)CrossRefGoogle Scholar
  15. 15.
    Martin, W., Aggrawal, J.: Volumetric descriptions of objects from multiple views. IEEE Transactions on Pattern Analysis and Machine Intelligence (1983)Google Scholar
  16. 16.
    Srinivasan, P., Liang, P., Hackwood, S.: Computational geometric methods in volumetric intersection for 3D reconstruction. Pattern Recognition 23, 843–857 (1990)CrossRefGoogle Scholar
  17. 17.
    Greg, F.P., Slabaugh, G., Culbertson, B., Schafer, R., Malzbender, T.: A survey of methods for volumetric scene reconstruction. In: International Workshop on Volume Graphics (2001)Google Scholar
  18. 18.
    Potmesil, M.: Generating octree models of 3D objects from their silhouettes in a sequence of images. Computer Vision, Graphics, and Image Processing 40, 1–29 (1987)CrossRefGoogle Scholar
  19. 19.
    Laurentini, A.: The visual hull concept for silhouette-based image understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 150–162 (1994)CrossRefGoogle Scholar
  20. 20.
    Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3D surface construction algorithm. Computer Graphics 21, 163–169 (1987)CrossRefGoogle Scholar
  21. 21.
    Newman, T.S., Yi, H.: A survey of the marching cubes algorithm. Computers and Graphics 30, 854–879 (2006)CrossRefGoogle Scholar
  22. 22.
    Klein, T., Stegmaier, S., Ertl, T.: Hardware-accelerated reconstruction of polygonal isosurface representations on unstructured grids. In: PG 2004: Proceedings of the Computer Graphics and Applications, 12th Pacific Conference, pp. 186–195. IEEE Computer Society, Washington, DC (2004)CrossRefGoogle Scholar
  23. 23.
    Pascucci, V.: Isosurface computation made simple: Hardware acceleration, adaptive refinement and tetrahedral stripping. In: Joint Eurographics - IEEE TVCG Symposium on Visualization (VisSym.), pp. 293–300 (2004)Google Scholar
  24. 24.
    Reck, F., Dachsbacher, C., Grosso, R., Greiner, G., Stamminger, M.: Realtime isosurface extraction with graphics hardware. In: Proceedings of Eurographics (2004)Google Scholar
  25. 25.
    Goetz, F., Junklewitz, T., Domik, G.: Real-time marching cubes on the vertex shader. In: Proceedings of Eurographics (2005)Google Scholar
  26. 26.
    Johansson, G., Carr, H.: Accelerating marching cubes with graphics hardware. In: CASCON 2006: Proceedings of the 2006 Conference of the Center for Advanced Studies on Collaborative Research, p. 378. ACM Press (2006)Google Scholar
  27. 27.
    NVIDIA. GPU Computing SDK (2009),
  28. 28.
    Harris, M., Sengupta, S., Owens, J.: CUDA Data Parallel Primitives Library (2007),
  29. 29.
    Sengupta, S., Harris, M., Zhang, Y., Owens, J.D.: Scan primitives for gpu computing. In: Graphics Hardware 2007, pp. 97–106. ACM (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • K. Tzevanidis
    • 1
  • X. Zabulis
    • 1
  • T. Sarmis
    • 1
  • P. Koutlemanis
    • 1
  • N. Kyriazis
    • 1
  • A. Argyros
    • 1
  1. 1.Institute of Computer Science (ICS)Foundation for Research and Technology - Hellas (Forth)HeraklionGreece

Personalised recommendations