Improving global exploration of MCMC light transport simulation

Markov Chain Monte Carlo (MCMC) has recently received a lot of attention in light transport simulation research [Hanika et al. 2015; Hachisuka et al. 2014]. While these methods aim at high quality sampling of local extremes of the path space (so called local exploration), the other issue - discovering these extremes - has been so far neglected. Poor global exploration results in oversampling some parts of the paths space, while undersampling or completely missing other parts (see Fig. 1). Such behavior of MCMC-based light transport algorithms limits their use in practice, since we can never tell for sure whether the image has already converged.