“A matrix sampling-and-recovery approach for many-lights rendering” by Huo, Wang, Jin, Liu and Bao
Conference:
Type(s):
Title:
- A matrix sampling-and-recovery approach for many-lights rendering
Session/Category Title: Sampling and Light Transport
Presenter(s)/Author(s):
Abstract:
Instead of computing on a large number of virtual point lights (VPLs), scalable many-lights rendering methods effectively simulate various illumination effects only using hundreds or thousands of representative VPLs. However, gathering illuminations from these representative VPLs, especially computing the visibility, is still a tedious and time-consuming task. In this paper, we propose a new matrix sampling-and-recovery scheme to efficiently gather illuminations by only sampling a small number of visibilities between representative VPLs and surface points. Our approach is based on the observation that the lighting matrix used in manylights rendering is of low-rank, so that it is possible to sparsely sample a small number of entries, and then numerically complete the entire matrix. We propose a three-step algorithm to explore this observation. First, we design a new VPL clustering algorithm to slice the rows and group the columns of the full lighting matrix into a number of reduced matrices, which are sampled and recovered individually. Second, we propose a novel prediction method that predicts visibility of matrix entries from sparsely and randomly sampled entries. Finally, we adapt the matrix separation technique to recover the entire reduced matrix and compute final shadings. Experimental results show that our method heavily reduces the required visibility sampling in the final gathering and achieves 3–7 times speedup compared with the state-of-the-art methods on test scenes.
References:
1. Boyd, S., and Vandenberghe, L. 2004. Convex optimization. Cambridge university press.
2. Candès, E., and Recht, B. 2009. Exact matrix completion via convex optimization. Found. Comput. Math. 9, 6 (Dec.), 717–772.
3. Candès, E. J., Li, X., Ma, Y., and Wright, J. 2011. Robust principal component analysis? Journal of the ACM (JACM) 58, 3, 11.
4. Dachsbacher, C., Křivánek, J., Hašan, M., Arbree, A., Walter, B., and Novák, J. 2014. Scalable realistic rendering with many-light methods. In Computer Graphics Forum, vol. 33, Wiley Online Library, 88–104.
5. Davidovič, T., Křivánek, J., Hašan, M., Slusallek, P., and Bala, K. 2010. Combining global and local virtual lights for detailed glossy illumination. ACM Trans. Graph. 29 (December), 143:1–143:8.
6. Georgiev, I., Křivánek, J., Popov, S., and Slusallek, P. 2012. Importance caching for complex illumination. In Computer Graphics Forum, vol. 31, Wiley Online Library, 701–710.
7. Halko, N., Martinsson, P.-G., and Tropp, J. A. 2011. Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions. SIAM review 53, 2, 217–288.
8. Hašan, M., Pellacini, F., and Bala, K. 2007. Matrix row-column sampling for the many-light problem. ACM Trans. Graph. 26, 3, 26:1–10.
9. Hašan, M., Křivánek, J., Walter, B., and Bala, K. 2009. Virtual spherical lights for many-light rendering of glossy scenes. ACM Trans. Graph. 28 (December), 143:1–143:6.
10. Huang, F.-C., and Ramamoorthi, R. 2010. Sparsely precomputing the light transport matrix for real-time rendering. 21st Eurographics Symposium on Rendering 29, 4 (June).
11. Kautz, J., and McCool, M. D. 1999. Interactive rendering with arbitrary brdfs using separable approximations. In Rendering Techniques, 247–260.
12. Keller, A. 1997. Instant radiosity. In Proceedings of the 24th annual conference on Computer graphics and interactive techniques, ACM Press/Addison-Wesley Publishing Co., New York, NY, USA, SIGGRAPH ’97, 49–56.
13. Liao, S., Lopez, M. A., and Leutenegger, S. T. 2001. High dimensional similarity search with space filling curves. In Data Engineering, 2001. Proceedings. 17th International Conference on, IEEE, 615–622.
14. Lin, Z., Chen, M., and Ma, Y. 2009. The augmented lagrange multiplier method for exact recovery of corrupted low-rank matrices. UIUC Technical Report UILU-ENG-09-2215.
15. McCool, M. D., Ang, J., and Ahmad, A. 2001. Homomorphic factorization of brdfs for high-performance rendering. In SIGGRAPH, 171–178.
16. Mohri, M., and Talwalkar, A. 2011. Can matrix coherence be efficiently and accurately estimated? In International Conference on Artificial Intelligence and Statistics, 534–542.
17. Ou, J., and Pellacini, F. 2011. Lightslice: matrix slice sampling for the many-lights problem. ACM Trans. Graph. 30, 6 (Dec.), 179:1–179:8.
18. Ritschel, T., Grosch, T., Kim, M. H., Seidel, H.-P., Dachsbacher, C., and Kautz, J. 2008. Imperfect shadow maps for efficient computation of indirect illumination. ACM Trans. Graph. 27, 5, 129:1–8.
19. Segovia, B., Iehl, J. C., Mitanchey, R., and Péroche, B. 2006. Bidirectional instant radiosity. In Proceedings of the 17th Eurographics Conference on Rendering Techniques, Eurographics Association, Aire-la-Ville, Switzerland, Switzerland, EGSR ’06, 389–397.
20. Segovia, B., Iehl, J., and Peroche, B. 2007. Metropolis instant radiosity. Computer Graphics Forum 26, 3, 425–434.
21. Shen, Y., Wen, Z., and Zhang, Y. 2014. Augmented lagrangian alternating direction method for matrix separation based on low-rank factorization. Optimization Methods and Software 29, 2, 239–263.
22. Sloan, P.-P., Hall, J., Hart, J., and Snyder, J. 2003. Clustered principal components for precomputed radiance transfer. ACM Trans. Graph. 22, 3 (July), 382–391.
23. Walter, B., Fernandez, S., Arbree, A., Bala, K., Donikian, M., and Greenberg, D. P. 2005. Lightcuts: a scalable approach to illumination. ACM Trans. Graph. 24, 3, 1098–1107.
24. Walter, B., Arbree, A., Bala, K., and Greenberg, D. P. 2006. Multidimensional lightcuts. In ACM Transactions on Graphics (TOG), vol. 25, ACM, 1081–1088.
25. Walter, B., Khungurn, P., and Bala, K. 2012. Bidirectional lightcuts. ACM Trans. Graph. 31, 4 (July), 59:1–59:11.
26. Wang, R., Huo, Y., Yuan, Y., Zhou, K., Hua, W., and Bao, H. 2013. Gpu-based out-of-core many-lights rendering. ACM Trans. Graph. 32, 6 (Nov.), 210:1–210:10.
