Robust light transport simulation via metropolised bidirectional estimators

Efficiently simulating light transport in various scenes with a single algorithm is a difficult and important problem in computer graphics. Two major issues have been shown to hinder the efficiency of the existing solutions: light transport due to multiple highly glossy or specular interactions, and scenes with complex visibility between the camera and light sources. While recent bidirectional path sampling methods such as vertex connection and merging/unified path sampling (VCM/UPS) efficiently deal with highly glossy or specular transport, they tend to perform poorly in scenes with complex visibility. On the other hand, Markov chain Monte Carlo (MCMC) methods have been able to show some excellent results in scenes with complex visibility, but they behave unpredictably in scenes with glossy or specular surfaces due to their fundamental issue of sample correlation. In this paper, we show how to fuse the underlying key ideas behind VCM/UPS and MCMC into a single, efficient light transport solution. Our algorithm is specifically designed to retain the advantages of both approaches, while alleviating their limitations. Our experiments show that the algorithm can efficiently render scenes with both highly glossy or specular materials and complex visibility, without compromising the performance in simpler cases.

[1]  Jaakko Lehtinen,et al.  Anisotropic Gaussian mutations for metropolis light transport through Hessian-Hamiltonian dynamics , 2015, ACM Trans. Graph..

[2]  Adam Arbree,et al.  Scalable Realistic Rendering with Many‐Light Methods , 2014, Eurographics.

[3]  Toshiya Hachisuka,et al.  Multiplexed metropolis light transport , 2014, ACM Trans. Graph..

[4]  Toshiya Hachisuka,et al.  Robust adaptive photon tracing using photon path visibility , 2011, TOGS.

[5]  Yoshifumi Kitamura,et al.  Replica Exchange Light Transport , 2009, Comput. Graph. Forum.

[6]  Jun-Hai Yong,et al.  Eurographics Symposium on Rendering 2011 Improved Stochastic Progressive Photon Mapping with Metropolis Sampling , 2022 .

[7]  Csaba Kelemen,et al.  Simple and Robust Mutation Strategy for Metropolis Light Transport Algorithm , 2001 .

[8]  Justin Talbot,et al.  Energy redistribution path tracing , 2005, ACM Trans. Graph..

[9]  Yves D. Willems,et al.  Bi-directional path tracing , 1993 .

[10]  Leonidas J. Guibas,et al.  Optimally combining sampling techniques for Monte Carlo rendering , 1995, SIGGRAPH.

[11]  Henrik Wann Jensen,et al.  Importance Driven Path Tracing using the Photon Map , 1995, Rendering Techniques.

[12]  Tobias Ritschel,et al.  On-line learning of parametric mixture models for light transport simulation , 2014, ACM Trans. Graph..

[13]  Derek Nowrouzezahrai,et al.  Unifying points, beams, and paths in volumetric light transport simulation , 2014, ACM Trans. Graph..

[14]  Wang,et al.  Replica Monte Carlo simulation of spin glasses. , 1986, Physical review letters.

[15]  Thomas Bashford-Rogers,et al.  A Significance Cache for Accelerating Global Illumination , 2012, Comput. Graph. Forum.

[16]  W. K. Hastings,et al.  Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .

[17]  Andrew Gelman,et al.  Handbook of Markov Chain Monte Carlo , 2011 .

[18]  Toshiya Hachisuka,et al.  Stochastic progressive photon mapping , 2009, ACM Trans. Graph..

[19]  Henrik Wann Jensen,et al.  Global Illumination using Photon Maps , 1996, Rendering Techniques.

[20]  Gareth O. Roberts,et al.  Towards optimal scaling of metropolis-coupled Markov chain Monte Carlo , 2011, Stat. Comput..

[21]  Frédo Durand,et al.  Eurographics Symposium on Rendering 2015 Probabilistic Connections for Bidirectional Path Tracing Bidirectional Path Tracing Probabilistic Connections for Bidirectional Path Tracing , 2022 .

[22]  Jacopo Pantaleoni,et al.  A path space extension for robust light transport simulation , 2012, ACM Trans. Graph..

[23]  Alexander Keller,et al.  Metropolis Light Transport for Participating Media , 2000, Rendering Techniques.

[24]  Yu-Chi Lai,et al.  Metropolis photon sampling with optional user guidance , 2005, EGSR '05.

[25]  Leonidas J. Guibas,et al.  Bidirectional Estimators for Light Transport , 1995 .

[26]  Anton Kaplanyan,et al.  Improved Half Vector Space Light Transport , 2015, Comput. Graph. Forum.

[27]  Steve Marschner,et al.  Manifold exploration , 2012, ACM Trans. Graph..

[28]  Leonidas J. Guibas,et al.  Robust Monte Carlo methods for light transport simulation , 1997 .

[29]  H. Jensen,et al.  Progressive photon mapping , 2008, SIGGRAPH 2008.

[30]  Bernard Péroche,et al.  Metropolis Instant Radiosity , 2007, Comput. Graph. Forum.

[31]  Philipp Slusallek,et al.  Light transport simulation with vertex connection and merging , 2012, ACM Trans. Graph..

[32]  Leonidas J. Guibas,et al.  Metropolis light transport , 1997, SIGGRAPH.

[33]  Matthias Zwicker,et al.  Progressive photon beams , 2011, ACM Trans. Graph..

[34]  John C. Hart,et al.  Arbitrary Importance Functions for Metropolis Light Transport , 2010, Comput. Graph. Forum.

[35]  Jaakko Lehtinen,et al.  Recent Advances in Adaptive Sampling and Reconstruction for Monte Carlo Rendering , 2015, Comput. Graph. Forum.

[36]  Mark Meyer,et al.  Recent advances in light transport simulation: some theory and a lot of practice , 2014, SIGGRAPH '14.

[37]  Matthias Zwicker,et al.  Progressive photon mapping: A probabilistic approach , 2011, TOGS.

[38]  Anton Kaplanyan,et al.  Path Space Regularization for Holistic and Robust Light Transport , 2013, Comput. Graph. Forum.