This book provides a fundamental understanding of global illumination algorithms. It
discusses a broad class of algorithms for realistic image synthesis and introduces a …

We introduce a new concept for accelerating realistic image synthesis algorithms. At the
core of this procedure is a novel physical error metric that correctly predicts the perceptual …

We apply Stein's Unbiased Risk Estimator (SURE) to adaptive sampling and reconstruction
to reduce noise in Monte Carlo rendering. SURE is a general unbiased estimator for mean …

We propose an adaptive sampling and reconstruction method for offline Monte Carlo
rendering. Our method produces sampling maps constrained by a user-defined budget that …

Image denoising based on deep learning has become a powerful tool to accelerate Monte
Carlo rendering. Deep learning techniques can produce smooth images using a low sample …

We propose an efficient and robust image‐space denoising method for noisy images
generated by Monte Carlo ray tracing methods. Our method is based on two new concepts …

This full day course will provide a detailed overview of state of the art in Monte Carlo ray
tracing. Recent advances in algorithms and available compute power have made Monte …

Monte Carlo noise appears as outliers and as interpixel incoherence in a typical image
rendered at low sampling density. Unfortunately, none of the previous approaches can …

Synthetically generating images and video frames of complex 3D scenes using some photo‐
realistic rendering software is often prone to artifacts and requires expert knowledge to tune …

We present a practical global error estimation technique for Monte Carlo ray tracing
combined with deep learning based denoising. Our method uses aggregated estimates of …