We describe a machine learning technique for reconstructing image sequences rendered
using Monte Carlo methods. Our primary focus is on reconstruction of global illumination …

We introduce a reconstruction algorithm that generates a temporally stable sequence of
images from one path-per-pixel global illumination. To handle such noisy input, we use …

Denoising has proven to be useful to efficiently generate high-quality Monte Carlo
renderings. Traditional pixel-based denoisers exploit summary statistics of a pixel's sample …

High-quality denoising of Monte Carlo low-sample renderings remains a critical challenge
for practical interactive ray tracing. We present a new learning-based denoiser that achieves …

We propose a novel approach for denoising Monte Carlo path traced images, which uses
data from individual samples rather than relying on pixel aggregates. Samples are …

Real-time denoising for Monte Carlo rendering remains a critical challenge with regard to
the demanding requirements of both high fidelity and low computation time. In this paper, we …

Disclosed approaches may leverage the actual spatial and reflective properties of a virtual
environment—such as the size, shape, and orientation of a bidirectional reflectance …

In various examples, the actual spatial properties of a virtual environment are used to
produce, for a pixel, an anisotropic filter kernel for a filter having dimensions and weights …

We present a novel algorithm to denoise deep Monte Carlo renderings, in which pixels
contain multiple colour values, each for a different range of depths. Deep images are a more …

We present a texture space caching and reconstruction system for Monte Carlo ray tracing.
Our system gathers and filters shading on-demand, including querying secondary rays …