Quickly Creating Illumination-Controllable Point-Based Models from Photographs

Conference paper
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 145)


Based on the theory of visual hulls, this paper presents a method to create point based models for real objects. Instead of using the expensive special equipments such as 3D laser scanners, this method deals with some silhouette images of the objects, and generates uniformly point-sampled models. We adopt a uniform-interval index table to organize the silhouette edges of each sample image, which provides much flexibility for point sampling. Moreover, combining the surface splatting technology and Layered Depth Buffers (LDB), we introduce a new algorithm to judge the visibilities of the points. The experimental results have shown the high accuracy of the visibility judgment.


point based modeling illumination layered depth buffer 


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  1. 1.
    Deering, M.F.: Data Complexity for Virtual Reality: Where do all the Triangles Go? In: Proc. IEEE Virtual Reality Annual International Symposium (VRAIS), Singapore, pp. 357–363 (1993)Google Scholar
  2. 2.
    Alexa, M., Behr, L., Cohen-Or, D., Fleishman, S., Levin, D., Silva, C.: Point Set Surfaces. In: Proc. IEEE Visualisation, pp. 21–28 (2001)Google Scholar
  3. 3.
    Grossman, J.P., Dally, W.: Point Sample Rendering. In: Proc. Rendering Techniques 1998, pp. 181–192 (1998)Google Scholar
  4. 4.
    Pajarola, R.: efficient level-of-details for point based rendering. In: Proc. IASTED Computer Graphics and Imaging (2003)Google Scholar
  5. 5.
    Zwicker, M., Pfister, H., Baar, J.V., Gross, M.: Surface splatting. In: Proc. SIGGRAPH, pp. 371–378 (2001)Google Scholar
  6. 6.
    Matusik, W., Buehler, C., Raskar, R., Gortler, S., Mcmillan, L.: Image-based visual hulls. In: Proc. SIGGRAPH, pp. 369–374 (2000)Google Scholar
  7. 7.
    Shade, J., Gortler, S., He, L., Szeliski, R.: Layered Depth Images. In: Proc. SIGGRAPH 1998, pp. 231–242 (1998)Google Scholar
  8. 8.
    Pfister, H., Zwicker, M., van, B.J., Gross, M.: Surfels: Surface elements as rendering primitives. In: Proc. SIGGRAPH 2000, pp. 335–342 (2000)Google Scholar
  9. 9.
    Lorensen, W.E., Cline, H.E.: Marching Cubes: A High Resolution 3D Surface Construction Algorithm. In: Proc. SIGGRAPH 1987, pp. 163–169 (1987)Google Scholar
  10. 10.
    Faugeras, O.: Three-dimensional Computer Vision: A Geometric Viewpoint. MIT Press (1993)Google Scholar
  11. 11.
    Laurentini, A.: The visual hull concept for silhouette based image understanding. IEEE Trans. Pattern Anal. And Mach. Intell. 16, 150–162 (1994)CrossRefGoogle Scholar
  12. 12.
    Matusik, W.: Image-Based 3D Photography using Opacity Hulls. In: Proc. SIGGRAPH 2002, pp. 427–437 (2002)Google Scholar
  13. 13.
    McMillan, L.: Computing Visibility Without Depth, Department of Computer Science University of North Carolina, NC 27599Google Scholar
  14. 14.
    McMillan, L.: A list-priority rendering algorithm for redisplaying projected surfaces, Technical Report 95–005, University of North Carolina (1995)Google Scholar
  15. 15.
    Wood, D., Azuma, D., Aldinger, K., Curless, B., Duchamp, T., Salesin, D., Stuetzle, W.: Surface Light Fields for 3D Photography. In: Proc. SIGGRAPH 2000, pp. 287–296 (2000)Google Scholar
  16. 16.
    Abidi, M.A., Chandra, T.: A new efficient and direct solution for pose estimation using quadrangular targets: algorithm and evaluation. IEEE Trans. Pattern Anal. And Mach. Intell. 17, 534–538 (1995)CrossRefGoogle Scholar
  17. 17.
    Debevec, P., Malk, J.: Recovering high dynamic range radiance maps from photographs. In: Proc. SIGGRAPH 1997, pp. 369–378 (1997)Google Scholar

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.College of Information ScienceBeijing Language and Culture UniversityBeijingChina

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