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 …

In this paper, we propose a novel explicit image filter called guided filter. Derived from a
local linear model, the guided filter computes the filtering output by considering the content …

Ride-hailing platforms (eg, Uber and Didi Chuxing) have become increasingly popular in
recent years. Efficiency has always been an important metric for such platforms. However …

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 …

We present a technique for performing high-dimensional filtering of images and videos in
real time. Our approach produces high-quality results and accelerates filtering by computing …

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 …