Statistical Denoising of Transient Rendering

Abstract Transient rendering simulates light in motion, measuring the time of flight from the light source to the camera. However, the stochastic nature of Monte Carlo is aggravated in transient rendering, since samples are now spread along the temporal domain. In our work, we propose to denoise transient Monte Carlo renders by exploiting the spatio‐temporal correlation of transient light transport, extending a recent statistical denoising formulation. By relying on statistics, we achieve a near‐optimal tradeoff between reduced variance and introduced bias. We efficiently collect per‐time‐bin statistics in the temporal domain while avoiding impractical memory requirements, and use these collected statistics to analyze the spatio‐temporal correlation and discriminate which time bins should be combined. Our statistics‐based transient denoiser does not hallucinate, guarantees convergence of the result, is efficient, does not require any training and naturally handles participating media. We believe that the generality of our method might pave the way for denoising time‐resolved Monte Carlo simulations in other domains, such as non‐line‐of‐sight imaging, acoustic rendering, or absorption microscopy.