Neural Importance Sampling

Here’s an early exploration into using neural networks for guiding Monte Carlo integration, from Disney Research. Interestingly, they note how their learned models can be adapted to slightly modified scenes (e.g. changes in camera) and that might make it quite applicable to optimizing renders of animations. We propose to use deep neural networks for generating…Read More

From Faces to Outdoor Light Probes

Image-based lighting has allowed the creation of photo-realistic computer-generated content. However, it requires the accurate capture of the illumination conditions, a task neither easy nor intuitive, especially to the average digital photography enthusiast. This paper presents an approach to directly estimate an HDR light probe from a single LDR photograph, shot outdoors with a consumer…Read More

Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks

Some great results from Disney, to be presented at this year’s SIGGRAPH Asia: “We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming aerosols make rendering of clouds—e.g. the characteristic silverlining and the…Read More