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

Efficiently rendering direct lighting from millions of dynamic light sources using Monte Carlo
integration remains a challenging problem, even for off-line rendering systems. We …

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 …

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 …

Monte Carlo (MC) path tracing is a popular realistic rendering technique widely used in
computer animation, film production, video games, etc. Compared with other rendering …

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 …

Path tracing produces realistic results including global illumination using a unified simple
rendering pipeline. Reducing the amount of noise to imperceptible levels without post …