The most successful approaches for filtering Monte Carlo noise use feature-based filters (eg,
cross-bilateral and cross non-local means filters) that exploit additional scene features such …

We propose a novel approach for image space adaptive sampling and filtering in Monte
Carlo rendering. We use an iterative scheme composed of three steps. First, we adaptively …

We introduce a novel approach for image space adaptive sampling and reconstruction in
Monte Carlo rendering. We greedily minimize relative mean squared error (MSE) by iterating …

We apply Stein's Unbiased Risk Estimator (SURE) to adaptive sampling and reconstruction
to reduce noise in Monte Carlo rendering. SURE is a general unbiased estimator for mean …

We propose a method that robustly combines color and feature buffers to denoise Monte
Carlo renderings. On one hand, feature buffers, such as per pixel normals, textures, or …

Monte Carlo ray tracing is considered one of the most effective techniques for rendering
photo-realistic imagery, but requires a large number of ray samples to produce converged or …

The commoditization of virtual and augmented reality devices and the availability of
inexpensive consumer depth cameras have catalyzed a resurgence of interest in …

This article proposes a new multiscale filter accelerating Monte Carlo renderer. Each pixel in
the image is characterized by the colors of the rays that reach its surface. The proposed filter …

Soft shadows, depth of field, and diffuse global illumination are common distribution effects,
usually rendered by Monte Carlo ray tracing. Physically correct, noise-free images can …

Light field reconstruction algorithms can substantially decrease the noise in stochastically
rendered images. Recent algorithms for defocus blur alone are both fast and accurate …