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

Learning to See in the Dark

Imaging in low light is challenging due to low photon count and low SNR. Short-exposure images suffer from noise, while long exposure can induce blur and is often impractical. A variety of denoising, deblurring, and enhancement techniques have been proposed, but their effectiveness is limited in extreme conditions, such as video-rate imaging at night. To…Read More

HDR image reconstruction from a single exposure using deep CNNs

After creating LDR images by applying simulated camera sensor saturation to real HDR photos, the authors trained a model which could perform the inverse LDR->HDR operation and also generalize to previously unseen images. What’s more, they have released their dataset which can be downloaded from their project page. Camera sensors can only capture a limited…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