Out-of-the-loop Autotuning of Metropolis Light Transport with Reciprocal Probability Binning

Abstract

The performance of Markov Chain Monte Carlo (MCMC) rendering methods depends heavily on the mutation strategies and their parameters. We treat the underlying mutation strategies as black-boxes and focus on their parameters. This avoids the need for tedious manual parameter tuning and enables automatic adaptation to the actual scene. We propose a framework for out-of-the-loop autotuning of these parameters. As a pilot example, we demonstrate our tuning strategy for small-step mutations in Primary Sample Space Metropolis Light Transport. Our σ-binning strategy introduces a set of mutation parameters chosen by a heuristic: the inverse probability of the local direction sampling, which captures some characteristics of the local sampling. We show that our approach can successfully control the parameters and achieve better performance compared to non-adaptive mutation strategies.