Efficiently rendering direct lighting from millions of dynamic light sources using Monte Carlo
integration remains a challenging problem, even for off-line rendering systems. We …

We present a method for reducing errors---variance and bias---in physically based
differentiable rendering (PBDR). Typical applications of PBDR repeatedly render a scene as …

We propose neural control variates (NCV) for unbiased variance reduction in parametric
Monte Carlo integration. So far, the core challenge of applying the method of control variates …

Various denoising methods have been proposed to clean up the noise in Monte Carlo (MC)
renderings, each having different advantages, disadvantages, and applicable scenarios. In …

Multiple importance sampling (MIS) is a provably good way to combine a finite set of
sampling techniques to reduce variance in Monte Carlo integral estimation. However, there …

The efficiency of inverse optimization in physically based differentiable rendering heavily
depends on the variance of Monte Carlo estimation. Despite recent advancements …

Importance sampling (IS) is a powerful Monte Carlo methodology for the approximation of
intractable integrals, very often involving a target probability distribution. The performance of …

To render higher quality images from the samples generated by path tracing with a low
sample count, we propose a novel path reuse approach that processes a fixed collection of …

Path tracing is now the standard method used to generate realistic imagery in many
domains, eg, film, special effects, architecture etc. Path guiding has recently emerged as a …

With the advent of real-time ray tracing, there is an increasing interest in GPU-friendly
importance sampling techniques. We present such methods to sample convex polygonal …