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 …

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 …

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 …

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 …

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 …

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

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 …