Physics-driven Fire Modeling from Multi-view Images

TL;DR

This paper introduces a novel method to reconstruct physically valid fire models from multi-view stereo images, enabling plausible estimation of physical properties like temperature and density from RGB cameras.

Contribution

It is the first to estimate physical properties of fire from RGB images, improving realism in fire modeling and illumination in virtual scenes.

Findings

Successfully reconstructs fire models from multi-view images.

Estimates physical properties such as temperature and density.

Enhances realism in virtual scene illumination.

Abstract

Fire effects are widely used in various computer graphics applications such as visual effects and video games. Modeling the shape and appearance of fire phenomenon is challenging as the underlying effects are driven by complex laws of physics. State-of-the-art fire modeling techniques rely on sophisticated physical simulations which require intensive parameter tuning, or use simplifications which produce physically invalid results. In this paper, we present a novel method of reconstructing physically valid fire models from multi-view stereo images. Our method, for the first time, provides plausible estimation of physical properties (e.g., temperature, density) of a fire volume using RGB cameras. This allows for a number of novel phenomena such as global fire illumination effects. The effectiveness and usefulness of our method are tested by generating fire models from a variety of input data, and applying the reconstructed fire models for realistic illumination of virtual scenes.