Kernel-Predicting Convolutional Networks for Denoising Monte Carlo Renderings Page 1
Kernel-Predicting Convolutional Networks for Denoising Monte Carlo Renderings STEVE …

We present a modular convolutional architecture for denoising rendered images. We
expand on the capabilities of kernel-predicting networks by combining them with a number …

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 …

Along with the rapid improvements in hardware and gradually increasing perceptual
demands of users, Monte Carlo path tracing is becoming more popular in movie production …

We address the problem of denoising Monte Carlo renderings by studying existing
approaches and proposing a new algorithm that yields state‐of‐the‐art performance on a …

Neural rendering algorithms introduce a fundamentally new approach for photorealistic
rendering, typically by learning a neural representation of illumination on large numbers of …

The Manuka rendering architecture has been designed in the spirit of the classic reyes
rendering architecture: to enable the creation of visually rich computer generated imagery …

We develop a new theory of volumetric light transport for media with non-exponential free-
flight distributions. Recent insights from atmospheric sciences and neutron transport …

Multiple Importance Sampling (MIS) is a key technique for achieving robustness of Monte
Carlo estimators in computer graphics and other fields. We derive optimal weighting …