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

TL;DR

This paper introduces a neural network-based method for efficiently rendering realistic atmospheric clouds by predicting radiance from learned cloud exemplars, enabling fast, high-quality image synthesis suitable for animation and design.

Contribution

It presents a novel approach combining hierarchical descriptors and neural networks to accurately predict cloud radiance, outperforming traditional Monte Carlo methods in speed and quality.

Findings

Achieves near-indistinguishable cloud images within seconds

Uses hierarchical descriptors to improve neural network learning

Provides a stable, high-quality rendering method for animation

Abstract

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 "whiteness" of the inner body---challenging for methods based solely on Monte Carlo integration or diffusion theory. We approach the problem differently. Instead of simulating all light transport during rendering, we pre-learn the spatial and directional distribution of radiant flux from tens of cloud exemplars. To render a new scene, we sample visible points of the cloud and, for each, extract a hierarchical 3D descriptor of the cloud geometry with respect to the shading location and the light source. The descriptor is input to a deep neural network that predicts the radiance function for each shading configuration. We make the key observation that progressively feeding the hierarchical descriptor into the network enhances the network's ability to learn faster and predict with high accuracy while using few coefficients. We also employ a block design with residual connections to further improve performance. A GPU implementation of our method synthesizes images of clouds that are nearly indistinguishable from the reference solution within seconds interactively. Our method thus represents a viable solution for applications such as cloud design and, thanks to its temporal stability, also for high-quality production of animated content.