Selectively Metropolised
Monte Carlo light transport simulation
Benedikt Bitterli Wojciech Jarosz
In ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia), 2019
For this indoor scene lit by a sun/sky environment emitter, path tracing resolves most of the lighting well, but introduces high variance in the reflective caustic cast by the mirror. ERPT and RJMLT resolve the caustic better, but do not handle diffuse lighting well and introduce noise and temporal flickering. Our method uses MLT to only resolve the difficult paths using bidirectional path tracing, and handles everything else with path tracing, leading to significantly improved MSE (relative MSE shown in insets) and vastly reduced temporal flickering. Scene ©SlykDrako.
Abstract
Light transport is a complex problem with many solutions. Practitioners are now faced with the difficult task of choosing which rendering algorithm to use for any given scene. Simple Monte Carlo methods, such as path tracing, work well for the majority of lighting scenarios, but introduce excessive variance when they encounter transport they cannot sample (such as caustics). More sophisticated rendering algorithms, such as bidirectional path tracing, handle a larger class of light transport robustly, but have a high computational overhead that makes them inefficient for scenes that are not dominated by difficult transport. The underlying problem is that rendering algorithms can only be executed indiscriminately on all transport, even though they may only offer improvement for a subset of paths. In this paper, we introduce a new scheme for selectively combining different Monte Carlo rendering algorithms. We use a simple transport method (e.g. path tracing) as the base, and treat high variance “fireflies” as seeds for a Markov chain that locally uses a Metropolised version of a more sophisticated transport method for exploration, removing the firefly in an unbiased manner. We use a weighting scheme inspired by multiple importance sampling to partition the integrand into regions the base method can sample well and those it cannot, and only use Metropolis for the latter. This constrains the Markov chain to paths where it offers improvement, and keeps it away from regions already handled well by the base estimator. Combined with stratified initialization, short chain lengths and careful allocation of samples, this vastly reduces non-uniform noise and temporal flickering artifacts normally encountered with a global application of Metropolis methods. Through careful design choices, we ensure our algorithm never performs much worse than the base estimator alone, and usually performs significantly better, thereby reducing the need to experiment with different algorithms for each scene.
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