Replica Exchange Light Transport

• Published in: Computer graphics forum Vol. 28; no. 8; pp. 2330 - 2342
• Main Authors: Kitaoka, Shinya, Kitamura, Yoshifumi, Kishino, Fumio
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.12.2009
• Subjects: Algorithms; Computational physics; Computer graphics; global illumination; I.3.3 [Computer Graphics]: Picture/Image Generation; I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism; Information processing; Light; Markov analysis; Metropolis light transport; Monte Carlo simulation; multiple importance sampling; Optimization algorithms; population annealing; replica exchange Monte Carlo method; Sampling techniques; Systems development
• ISSN: 0167-7055; 1467-8659
• DOI: 10.1111/j.1467-8659.2009.01540.x

We solve the light transport problem by introducing a novel unbiased Monte Carlo algorithm called replica exchange light transport, inspired by the replica exchange Monte Carlo method in the fields of computational physics and statistical information processing. The replica exchange Monte Carlo method is a sampling technique whose operation resembles simulated annealing in optimization algorithms using a set of sampling distributions. We apply it to the solution of light transport integration by extending the probability density function of an integrand of the integration to a set of distributions. That set of distributions is composed of combinations of the path densities of different path generation types: uniform distributions in the integral domain, explicit and implicit paths in light (particle/photon) tracing, indirect paths in bidirectional path tracing, explicit and implicit paths in path tracing, and implicit caustics paths seen through specular surfaces including the delta function in path tracing. The replica‐exchange light transport algorithm generates a sequence of path samples from each distribution and samples the simultaneous distribution of those distributions as a stationary distribution by using the Markov chain Monte Carlo method. Then the algorithm combines the obtained path samples from each distribution using multiple importance sampling. We compare the images generated with our algorithm to those generated with bidirectional path tracing and Metropolis light transport based on the primary sample space. Our proposing algorithm has better convergence property than bidirectional path tracing and the Metropolis light transport, and it is easy to implement by extending the Metropolis light transport.