A data-driven compression method for transient rendering

Monte Carlo methods for transient rendering have become a powerful instrument to generate reliable data in transient imaging applications, either for benchmarking, analysis, or as a source for data-driven approaches. However, due to the increased dimensionality of time-resolved renders, storage and data bandwidth are significant limiting constraints, where a single time-resolved render of a scene can take several hundreds of megabytes. In this work we propose a learning-based approach that makes use of deep encoder-decoder architectures to learn lower-dimensional feature vectors of time-resolved pixels. We demonstrate how our method is capable of compressing transient renders up to a factor of 32, and recover the full transient profile making use of a decoder. Additionally, we show how our learned features significantly mitigate variance on the recovered signal, addressing one of the pathological problems in transient rendering.