Learning to Importance Sample in Primary Sample Space

Understand

Importance sampling is one of the most widely used variance reduction strategies in Monte Carlo rendering.

• In this paper, we propose a novel importance sampling technique that uses a neural network to learn how to sample from a desired density represented by a set of samples.
• Our approach considers an existing Monte Carlo rendering algorithm as a black box.
• During a scene-dependent training phase, we learn to generate samples with a desired density in the primary sample space of the rendering algorithm using maximum likelihood estimation.