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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Culbertson, W.B.; Malzbender, T.; Slabaugh, G.G. (2000)
Publisher: Springer
Languages: English
Types: Part of book or chapter of book
Subjects: QA75

Classified by OpenAIRE into

ACM Ref: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, ComputingMethodologies_COMPUTERGRAPHICS
Image-based reconstruction from randomly scattered views is a challenging problem. We present a new algorithm that extends Seitz and Dyer’s Voxel Coloring algorithm. Unlike their algorithm, ours can use images from arbitrary camera locations. The key problem in this class of algorithms is that of identifying the images from which a voxel is visible. Unlike Kutulakos and Seitz’s Space Carving technique, our algorithm solves this problem exactly and the resulting reconstructions yield better results in our application, which is synthesizing new views. One variation of our algorithm minimizes color consistency comparisons; another uses less memory and can be accelerated with graphics hardware. We present efficiency measurements and, for comparison, we present images synthesized using our algorithm and Space Carving.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • 1. S. Seitz, Charles R. Dyer, “Photorealistic Scene Reconstruction by Voxel Coloring”, Proceedings of Computer Vision and Pattern Recognition Conference, 1997, pp. 1067- 1073.
    • 2. K. N. Kutulakos and S. M. Seitz, “What Do N Photographs Tell Us about 3D Shape?” TR680, Computer Science Dept. U. Rochester, January 1998.
    • 3. S. Seitz, Charles R. Dyer, “View Morphing”, Proceedings of SIGGRAPH 1996, pp. 21-30.
    • 4. M. Levoy and P. Hanrahan, “Light Field Rendering”, Proceedings of SIGGRAPH 1996, pp. 31-42.
    • 5. S. Gortler, R. Grzeszczuk, R. Szeliski, M. Cohen, “The Lumigraph”, Proceedings of SIGGRAPH 1996, pp. 43-54.
    • 6. H. Shum, H. Li-Wei, “Rendering with Concentric Mosaics”, Proceedings of SIGGRAPH 99, pp. 299-306.
    • 7. W. E. L. Grimson, “Computational experiments with a feature based stereo algorithm”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 7, no. 1, January 1985, pp. 17-34.
    • 8. M. Okutomi, T. Kanade, “A Multi-baseline Stereo”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, No. 4, April 1993, pp. 353-363.
    • 9. P. J. Narayanan, P. Rander, T. Kanade, “Constucting Virtual Worlds Using Dense Stereo”, IEEE International Conference on Computer Vision, 1998, pp. 3-10.
    • 10. S. Roy, I. Cox, “A Maximum-Flow Formulation of the N-camera Stereo Correspondence Problem”, IEEE International Conference on Computer Vision, 1998, pp. 492-499.
    • 11. R. Szeliski, P. Golland, “Stereo Matching with Transparency and Matting”, IEEE International Conference on Computer Vision, 1998, pp. 517-524.
    • 12. O. Faugeras, R. Keriven, “Complete Dense Stereovision using Level Set Methods”, Fifth European Conference on Computer Vision, 1998.
    • 13. H. Saito, T. Kanade, “Shape Reconstruction in Projective Grid Space from Large Number of Images”, Proceedings of Computer Vision and Pattern Recognition Conference, 1999, volume 2, pp. 49-54.
    • 14. N. Max, “Hierarchical Rendering of Trees from Precomputed Multi-Layer Z-Buffers”, Eurographics Rendering Workshop 1996, pp 165-174.
    • 15. J. Shade, S. Gortler, L. He, R. Szeliski, “Layered Depth Images”, Proceedings of SIGGRAPH 98, pp. 231-242.
    • 16. H. Weghorst, G. Hooper, D. P. Greenberg, “Improving Computational Methods for Ray Tracing”, ACM Transactions on Graphics, 3(1), January 1984, pp. 52-69.
    • 17. Eos Systems Inc., 205-2034 West 12th Ave., Vancouver B.C. V6J 2G2, Canada.
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