Irradiance regression for efficient final gathering in global illumination

doi:10.1007/s11704-014-4211-6

Front. Comput. Sci.

2015, Vol. 9

Issue (3) : 456-465 https://doi.org/10.1007/s11704-014-4211-6

RESEARCH ARTICLE

Irradiance regression for efficient final gathering in global illumination

Xuezhen HUANG¹,Xin SUN²,Zhong REN^1,^*(

),Xin TONG²,Baining GUO²,Kun ZHOU¹

1. State Key Laboratory of Computer Aided Design and Computer Graphics, Zhejiang University, Hangzhou 310058, China
2. Microsoft Research Asia, Beijing 100029, China

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Abstract

Photon mapping is widely used for global illumination rendering because of its high computational efficiency. But its efficiency is still limited, mainly by the intensive sampling required in final gathering, a process that is critical for removing low frequency artifacts of density estimation. In this paper, we propose a method to predict the final gathering estimation with direct density estimation, thereby achieving high quality global illumination by photon mapping with high efficiency. We first sample the irradiance of a subset of shading points by both final gathering and direct radiance estimation. Then we use the samples as a training set to predict the final gathered irradiance of other shading points through regression. Consequently, we are able to achieve about three times overall speedup compared with straightforward final gathering in global illumination computation with the same rendering quality.

Keywords global illumination photon mapping final gathering radiance estimation regression

Corresponding Author(s): Zhong REN

Issue Date: 18 May 2015

Cite this article:

Xin SUN,Zhong REN,Xin TONG, et al. Irradiance regression for efficient final gathering in global illumination[J]. Front. Comput. Sci., 2015, 9(3): 456-465.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-014-4211-6
https://academic.hep.com.cn/fcs/EN/Y2015/V9/I3/456

1	Kajiya J T. The rendering equation. SIGGRAPH Comput. Graph., 1986, 20(4): 143-150 https://doi.org/10.1145/15886.15902
2	Jensen H W. Realistic image synthesis using photon mapping. Natick, MA: A. K. Peters, Ltd., 2001 https://doi.org/10.1201/b10685
3	Dutré P, Jensen H W, Arvo J, Bala K, Bekaert P, Marschner S, Pharr M. State of the art in monte carlo global illumination. In: Proceedings of ACM SIGGRAPH 2004 Course Notes. 2004 https://doi.org/10.1145/1103900.1103905
4	Wald I, Mark W R, Günther J, Boulos S, Ize T, Hunt W, Parker S G, Shirley P. State of the art in ray tracing animated scenes. Computer Graphics Forum, 2007, 28(6): 1691-1722 https://doi.org/10.1111/j.1467-8659.2008.01313.x
5	Ritschel T, Dachsbacher C, Grosch T, Kautz J. The state of the art in interactive global illumination. Computer Graphics Forum, 2012, 31(1): 160-188 https://doi.org/10.1111/j.1467-8659.2012.02093.x
6	Lafortune E P, Willems Y D. Bi-directional path tracing. In: Proceedings of ACM SIGGRAPH. 1993, 145-153
7	Keller A. Instant radiosity. In: Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’97. 1997, 49-56 https://doi.org/10.1145/258734.258769
8	Jensen H W, Christensen N J. Photon maps in bidirectional Monte Carlo ray tracing of complex objects. Computers & Graphics, 1995, 19(2): 215-224 https://doi.org/10.1016/0097-8493(94)00145-O
9	Ward G J, Rubinstein FM, Clear R D. A ray tracing solution for diffuse interreflection. SIGGRAPH Comput. Graph., 1988, 22(4): 85-92 https://doi.org/10.1145/378456.378490
10	Ward G J, Heckbert P S. Irradiance gradients. In: Proceedings of 3rd Eurographics Workshop on Rendering. 1992, 85-98
11	K?ivánek J, Gautron P, Ward G, Jensen H W, Christensen P H, Tabellion E. Practical global illumination with irradiance caching. In: Proceedings of ACM SIGGRAPH, 2008, 60:1-60:20 https://doi.org/10.1145/1401132.1401213
12	Schwarzhaupt J, Jensen H W, Jarosz W. Practical hessian-based error control for irradiance caching. ACM Transactions on Graphics, 2012, 31(6): 193 https://doi.org/10.1145/2366145.2366212
13	Spencer B, Jones M W. Into the blue: better caustics through photon relaxation. Computer Graphics Forum, 2009, 28(2): 319-328 https://doi.org/10.1111/j.1467-8659.2009.01371.x
14	Hachisuka T, Ogaki S, Jensen HW. Progressive photon mapping. ACM Transactions on Graphics, 2008, 27(5): 130:1-130:8
15	Hachisuka T, Jarosz W, Bouchard G, Christensen P, Frisvad J R, Jakob W, Jensen HW, Kaschalk M, Knaus C, Selle A, Spencer B. State of the art in photon density estimation. In: Proceedings of ACM SIGGRAPH 2012 Courses, SIGGRAPH ’12. 2012, 6:1-6:469 https://doi.org/10.1145/2343483.2343489
16	Brouillat J, Bouville C, Loos B, Hansen C, Bouatouch K. A Bayesian monte carlo approach to global illumination. Computer Graphics Forum, 2009, 28(8): 2315-2329 https://doi.org/10.1111/j.1467-8659.2009.01537.x
17	Marques R, Bouville C, Ribardière M, Santos L P, Bouatouch K. A spherical gaussian framework for Bayesian Monte Carlo rendering of glossy surfaces. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(10): 1619-1632 https://doi.org/10.1109/TVCG.2013.79
18	Sloan P P, Kautz J, Snyder J. Precomputed radiance transfer for real-time rendering in dynamic, low-frequency lighting environments. ACM Transactions on Graphics, 2002, 21(3): 527-536 https://doi.org/10.1145/566654.566612
19	Wang J, Xu K, Zhou K, Lin S, Hu S M, Guo B. Spherical harmonics scaling. The Visual Computer, 2006, 22: 713-720 https://doi.org/10.1007/s00371-006-0057-8
20	Ng R, Ramamoorthi R, Hanrahan P. All-frequency shadows using nonlinear wavelet lighting approximation. ACM Transactions on Graphics, 2003, 22(3): 376-381 https://doi.org/10.1145/882262.882280
21	Tsai Y T, Shih Z C. All-frequency precomputed radiance transfer using spherical radial basis functions and clustered tensor approximation. ACM Transactions on Graphics, 2006, 25(3): 967-976 https://doi.org/10.1145/1141911.1141981
22	Xu K, Jia Y T, Fu H, Hu S M, Tai C L. Spherical piecewise constant basis functions for all-frequency precomputed radiance transfer. IEEE Transaction on Visualization and Computer Graphics, 2008, 14(2): 454-467 https://doi.org/10.1109/TVCG.2007.70442
23	Zhang Y, Dong Z, Ma K L. Real-time volume rendering in dynamic lighting environments using precomputed photon mapping. IEEE Transaction on Visualization Computer Graphics, 2013, 19(8): 1317-1330 https://doi.org/10.1109/TVCG.2013.17
24	Zhou K, Hu Y, Lin S, Guo B, Shum H Y. Precomputed shadow fields for dynamic scenes. ACM Transactions on Graphics, 2005, 24(3): 1196-1201 https://doi.org/10.1145/1073204.1073332
25	Ren Z, Wang R, Snyder J, Zhou K, Liu X, Sun B, Sloan P P, Bao H, Peng Q, Guo B. Real-time soft shadows in dynamic scenes using spherical harmonic exponentiation. ACM Transactions on Graphics, 2006, 25(3): 977-986 https://doi.org/10.1145/1141911.1141982
26	Sun X, Zhou K, Chen Y, Lin S, Shi J, Guo B. Interactive relighting with dynamic brdfs. ACM Transactions on Graphics, 2007, 26(3): 27 https://doi.org/10.1145/1276377.1276411
27	Wang R, Tran J, Luebke D. All-frequency interactive relighting of translucent objects with single and multiple scattering. ACM Transactions on Graphics, 2005, 24(3): 1202-1207 https://doi.org/10.1145/1073204.1073333
28	Xu K, Gao Y, Li Y, Ju T, Hu S M. Real-time homogenous translucent material editing. Computer Graphics Forum, 2007, 26(3): 545-552 https://doi.org/10.1111/j.1467-8659.2007.01077.x
29	Wang R, Cheslack-Postava E, Wang R, Luebke D, Chen Q, Hua W, Peng Q, Bao H. Real-time editing and relighting of homogeneous translucent materials. The Visual Computer, 2008, 24(7-9): 565-575 https://doi.org/10.1007/s00371-008-0237-9
30	Dachsbacher C, Stamminger M. Reflective shadow maps. In: Proceedings of the 2005 Symposium on Interactive 3D Graphics and Games, I3D ’05. 2005, 203-231
31	Dachsbacher C, Stamminger M. Splatting indirect illumination. In: Proceedings of the 2006 Symposium on Interactive 3D Graphics and Games, I3D ’06. 2006, 93-100
32	Ritschel T, Grosch T, Kim M H, Seidel H P, Dachsbacher C, Kautz J. Imperfect shadow maps for efficient computation of indirect illumination. ACM Transactions on Graphics, 2008, 27(5): 129:1-129:8
33	Ritschel T, Engelhardt T, Grosch T, Seidel H P, Kautz J, Dachsbacher C. Micro-rendering for scalable, parallel final gathering. ACM Transactions on Graphics, 2009, 28(5): 132:1-132:8
34	Xu K, Cao Y P, Ma L Q, Dong Z, Wang R, Hu S M. A practical algorithm for rendering interreflections with all-frequency brdfs. ACM Transactions on Graphics, 2014, 33(1): 10:1-10:16
35	Ramamoorthi R, Hanrahan P. An efficient representation for irradiance environment maps. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH ’01. 2001, 497-500 https://doi.org/10.1145/383259.383317
36	Wang R, Wang R, Zhou K, Pan M, Bao H. An efficient gpu-based approach for interactive global illumination. ACM Transactions<?Pub Caret?> on Graphics, 2009, 28(3): 91:1-91:8
37	Wang J, Ren P, Gong M, Snyder J, Guo B. All-frequency rendering of dynamic, spatially-varying reflectance. ACM Transactions on Graphics, 2009, 28(5): 133:1-133:10
38	Xu K, Sun W L, Dong Z, Zhao D Y, Wu R D, Hu S M. Anisotropic spherical gaussians. ACM Transactions on Graphics, 2013, 32(6): 209:1-209:11
39	Ren P, Wang J, Gong M, Lin S, Tong X, Guo B. Global illumination with radiance regression functions. ACM Transactions on Graphics, 2013, 32(4): 130:1-130:12
40	Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290(5500): 2323-2326 https://doi.org/10.1126/science.290.5500.2323

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