Monte Carlo integration is firmly established as the basis for most practical realistic image
synthesis algorithms because of its flexibility and generality. However, the visual quality of …

The interaction of light and matter in the world surrounding us is of striking complexity and
beauty. Since the very beginning of computer graphics, adequate modelling of these …

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

We present a new technique for the display of high-dynamic-range images, which reduces
the contrast while preserving detail. It is based on a two-scale decomposition of the image …

Because of the increase flow of network traffic and its significance to the provision of
ubiquitous services, cyberattacks attempt to compromise the security principles of …

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 …

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 …

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 …