Abstract
In this article, we propose an efficient and accurate compressive-sensing-based method for estimating the light transport characteristics of real-world scenes. Although compressive sensing allows the efficient estimation of a high-dimensional signal with a sparse or near-to-sparse representation from a small number of samples, the computational cost of the compressive sensing in estimating the light transport characteristics is relatively high. Moreover, these methods require a relatively smaller number of images than other techniques although they still need 500–1000 images to estimate an accurate light transport matrix. Precomputed compressive sensing improves the performance of the compressive sensing by providing an appropriate initial state. This improvement is achieved in two steps: 1) pseudo-single-pixel projection by multiline projection and 2) regularized orthogonal matching pursuit (ROMP) with initial signal. With these two steps, we can estimate the light transport characteristics more accurately, much faster, and with a lesser number of images.
Article PDF
Similar content being viewed by others
Avoid common mistakes on your manuscript.
References
Y.-Y. Chuang, D. E. Zongker, J. Hindorff, B. Curless, D. H. Salesin, and R. Szeliski: Proc. 27th Annu. Conf. Computer Graphics and Interactive Techniques (SIGGRAPH’ 00), 2000, p. 121.
P. Peers and P. Dutré: Proc. 14th Eurographics Workshop Rendering (EGRW’ 03), 2003, p. 157.
D. E. Zongker, D. M. Werner, B. Curless, and D. H. Salesin: Proc. 26th Annu. Conf. Computer Graphics and Interactive Techniques (SIGGRAPH’ 99), 1999, p. 205.
P. Sen, B. Chen, G. Garg, S. R. Marschner, M. Horowitz, M. Levoy, and H. Lensch: ACM Trans. Graphics 24 (2005) 745.
G. Garg, E.-V. Talvala, M. Levoy, and H. P. A. Lensch: Proc. Eurographics Symp. Rendering, 2006, p. 251.
J. Wang, Y. Dong, X. Tong, Z. Lin, and B. Guo: ACM Trans. Graphics 28 (2009) No. 29, 1.
P. Peers, D. K. Mahajan, B. Lamond, A. Ghosh, W. Matusik, R. Ramamoorthi, and P. Debevec: ACM Trans. Graphics 28 (2009) No. 3, 1.
P. Sen and S. Darabi: Comput. Graphics Forum 28 (2009) 609.
P. Sen and S. Darabi: IEEE Trans. Visualization Comput. Graphics 17 (2011) 487.
D. Needell and R. Vershynin: Found. Comput. Math. 9 (2009) 317.
P. Debevec, T. Hawkins, C. Tchou, H.-P. Duiker, W. Sarokin, and M. Sagar: Proc. 27th Annu. Conf. Computer Graphics and Interactive Techniques (SIGGRAPH’ 00), 2000, p. 145.
A. Wenger, A. Gardner, C. Tchou, J. Unger, T. Hawkins, and P. Debevec: ACM Trans. Graphics 24 (2005) 756.
T. Hawkins, P. Einarsson, and P. Debevec: Proc. Eurographics Symp. Rendering, 2005, p. 91.
V. Masselus, P. Peers, P. Dutré, and Y. D. Willems: ACM Trans. Graphics 22 (2003) 613.
W. Matusik, M. Loper, and H. Pfister: Proc. Eurographics Symp. Rendering, 2004, p. 299.
J. Gu, S. Nayar, E. Grinspun, P. Belhumeur, and R. Ramamoorthi: Proc. 10th European Conf. Computer Vision (ECCV 2008), 2008, p. 845.
E. Candès and T. Tao: IEEE Trans. Inf. Theory 52 (2006) 5406.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Yamamoto, S., Itakura, Y., Sawabe, M. et al. Precomputed compressive sensing for light transport acquisition. OPT REV 18, 264–272 (2011). https://doi.org/10.1007/s10043-011-0053-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10043-011-0053-8