FlameGS: Reconstruct flame light field via Gaussian Splatting

TL;DR

This paper introduces FlameGS, a novel flame light field reconstruction method using Gaussian Splatting, which significantly reduces computation time and memory while maintaining high image fidelity.

Contribution

The paper presents a new flame modeling approach inspired by flame simulation technology, improving efficiency over traditional ART algorithms.

Findings

Achieves an average SSIM of 0.96 between actual and predicted images.

Reduces computation time by approximately 34 times.

Reduces memory usage by about 10 times.

Abstract

To address the time-consuming and computationally intensive issues of traditional ART algorithms for flame combustion diagnosis, inspired by flame simulation technology, we propose a novel representation method for flames. By modeling the luminous process of flames and utilizing 2D projection images for supervision, our experimental validation shows that this model achieves an average structural similarity index of 0.96 between actual images and predicted 2D projections, along with a Peak Signal-to-Noise Ratio of 39.05. Additionally, it saves approximately 34 times the computation time and about 10 times the memory compared to traditional algorithms.