“A virtual restoration stage for real-world objects” – ACM SIGGRAPH HISTORY ARCHIVES

“A virtual restoration stage for real-world objects”

  • ©

Conference:


Type(s):


Title:

    A virtual restoration stage for real-world objects

Session/Category Title:   Colourisation & upsampling


Presenter(s)/Author(s):



Abstract:


    In this paper, we introduce a system to virtually restore damaged or historically significant objects without needing to physically change the object in any way. Our work addresses both creating a restored synthetic version of the object as viewed from a camera and projecting the necessary light, using digital projectors, to give the illusion of the object being restored. The restoration algorithm uses an energy minimization method to enforce a set of criteria over the surface of the object and provides an interactive tool to the user which can compute a restoration in a few minutes. The visual compensation method develops a formulation that is particularly concerned with obtaining bright compensations under a specified maximum amount of light. The bound on the amount of light is of crucial importance when viewing and restoring old and potentially fragile objects. Finally, we demonstrate our system by restoring several deteriorated and old objects enabling the observer to view the original or restored object at will.

References:


    1. Aliaga, D., and Xu, Y. 2008. Photogeometric Structured Light: A Self-Calibrating and Multi-Viewpoint Framework for Accurate 3D Modeling. In Proc. of IEEE Computer Vision and Pattern Recognition, 1–8.Google Scholar
    2. Azuma, R., Baillot, Y., Behringer, R., Feiner, S., Julier, S., and MacIntyre, B. 2001. Recent Advances in Augmented Reality, IEEE Comp. Graphics & Applications, 21, 6, 34–47. Google Scholar
    3. Barrett, W. A., and Cheney, A. S. 2002. Object-based Image Editing. ACM Trans. on Graphics, 21, 3, 777–784. Google ScholarDigital Library
    4. Bertalmio, M., Sapiro, G., Caselles, V., and Ballester, V. C. 2000. Image Inpainting. In Proc. of ACM SIGGRAPH 2000, 417–424. Google Scholar
    5. Bertalmio, M., Vese, L., Sapiro, G., and Osher, S. 2003. Simultaneous Structure and Texture Image Inpainting. IEEE Trans. on Image Processing, 12, 8, 882–889. Google ScholarDigital Library
    6. Bimber, O., Frohlich, B., Schmalsteig, D., and Encarnação, L. M., 2001. The Virtual Showcase, IEEE Computer Graphics & Applications, 21, 6, 48–55. Google Scholar
    7. Bornard, R., Lecan, E., Laborelli, L., and Chenot, J. H. 2002. Missing Data Correction in Still Images and Image Sequences. International Multimedia Conference, 355–361. Google Scholar
    8. Chan, T. F., and Shen, J., 2001, Nontexture Inpainting by Curvature-Driven Diffusions. Journal of Visual Communication and Image Representation, 12, 4, 436–449.Google ScholarDigital Library
    9. Comaniciu, D., and Meer, P. 2002. Mean Shift: A Robust Approach Toward Feature Space Analysis. IEEE Trans. Pattern Analysis Machine Intelligence, 24, 5, 603–619. Google ScholarDigital Library
    10. Criminisi, A., Perez, P., and Toyama, K. 2003. Object Removal by Exemplar-based Inpainting. In Proc. of IEEE Computer Vision and Pattern Recognition, 2, 721–728.Google Scholar
    11. Drori, I., Cohen-Or, D., and Yeshurun, H. 2003. Fragment-based Image Completion. ACM Trans. on Graphics, 22, 3, 303–312. Google ScholarDigital Library
    12. Durand, F., and Dorsey, J. 2002. Fast Bilateral Filtering for the Display of High-Dynamic-Range Images. In Proc. of ACM SIGGRAPH 2002, 257–266. Google Scholar
    13. Efros, A. A., and Leung T. K. 1999. Texture Synthesis by Non-Parametric Sampling. In Proc. of IEEE International Conference on Computer Vision, 2, 1033–1038. Google ScholarDigital Library
    14. Felzenszwalb, P. F., and Huttenlocher, D. P. 2004. Efficient Graph-Based Image Segmentation. International Journal of Computer Vision, 59, 2, 167–181. Google ScholarDigital Library
    15. Fujii, K., Grossberg, M. D., and Nayar, S. K. 2005. A Projector-Camera System with Real-Time Photometric Adaptation for Dynamic Environments. In Proc. of IEEE Computer Vision and Pattern Recognition, 1, 814–821. Google Scholar
    16. Grossberg, M. D., Peri, H., Nayar, S. K., and Belhumeur, P. N. 2004. Making One Object Look Like Another: Controlling Appearance using a Projector-Camera System. In Proc. of IEEE Computer Vision and Pattern Recognition, 1, 452–459.Google Scholar
    17. Igehy, H., and Pereira, L. 1997. Image Replacement through Texture Synthesis. In Proc. of International Conference on Image Processing, 3, 186–189. Google ScholarDigital Library
    18. Jia, J., and Tang, C. K. 2003. Image Repairing: Robust Image Synthesis by Adaptive ND Tensor Voting. In Proc. of IEEE Computer Vision and Pattern Recognition, 1, 643–650. Google ScholarDigital Library
    19. Levin, A., Zomet, A., and Weiss, Y. 2003. Learning How to Inpaint from Global Image Statistics. In Proc. of IEEE International Conference on Computer Vision, 1, 305–312. Google ScholarDigital Library
    20. Mitsunaga, T., Nayar, S. K. 1999. Radiometric Self Calibration. IEEE Comp. Vision & Pattern Recognition, 1, 374–380.Google Scholar
    21. Nayar, S. K., Peri, H., Grossberg, M. D., and Belhumeur, P. N. 2003. A Projection System with Radiometric Compensation for Screen Imperfections. ICCV Workshop on Projector-Camera Systems.Google Scholar
    22. Perona, P., and Malik, J. 1990. Scale-Space and Edge Detection Using Anisotropic Diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 7, 629–639. Google ScholarDigital Library
    23. Raskar, R., Welch, G., Low, K. L., and Bandyopadhyay, D. 2001. Shader Lamps: Animating Real Objects With Image-Based Illumination. In Proc. of Eurographics Workshop on Rendering Techniques, 89–102. Google ScholarDigital Library
    24. Sen, P., Chen, B., Garg, G., Marschner, S. R., Horowitz, M., Levoy, M., and Lensch, H. P. A. 2005. Dual Photography. In Proc. of ACM SIGGRAPH 2005, 745–755. Google Scholar
    25. Sun, J., Yuan, L., Jia, J., and Shum, H. Y. 2005. Image Completion with Structure Propagation. ACM Trans. on Graphics, 24, 3, 861–868. Google ScholarDigital Library
    26. Tomasi, C., and Manduchi, R. 1998. Bilateral Filtering for Gray and Color Images. In Proc. of IEEE International Conf. on Computer Vision, 836–846. Google ScholarDigital Library
    27. Wei, L. Y., and Levoy, M. 2000. Fast Texture Synthesis Using Tree-structured Vector Quantization. In Proc. of ACM SIGGRAPH 2000, 479–488. Google Scholar
    28. Weiss, B. 2006. Fast Median and Bilateral Filtering. ACM Trans. of Graphics, 25, 3, 519–526. Google ScholarDigital Library
    29. Wetzstein, G., and Bimber, O. 2007. Radiometric Compensation through Inverse Light Transport. In Proc. of Pacific Graphics, 391–399. Google Scholar


ACM Digital Library Publication:



Overview Page:



Submit a story:

If you would like to submit a story about this presentation, please contact us: historyarchives@siggraph.org