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Importance sampling is one of the most widely used variance reductionstrategies in Monte Carlo rendering. In this paper, we propose a novelimportance sampling technique that uses a neural network to learn how to samplefrom a desired density represented by a set of samples. Our approach considersan existing Monte Carlo rendering algorithm as a black box. During ascene-dependent training phase, we learn to generate samples with a desireddensity in the primary sample space of the rendering algorithm using maximumlikelihood estimation. We leverage a recent neural network architecture thatwas designed to represent real-valued non-volume preserving ('Real NVP')transformations in high dimensional spaces. We use Real NVP to non-linearlywarp primary sample space and obtain desired densities. In addition, Real NVPefficiently computes the determinant of the Jacobian of the warp, which isrequired to implement the change of integration variables implied by the warp.A main advantage of our approach is that it is agnostic of underlying lighttransport effects, and can be combined with many existing rendering techniquesby treating them as a black box. We show that our approach leads to effectivevariance reduction in several practical scenarios.

Learning to Importance Sample in Primary Sample Space