FlameGS: Reconstruct flame light field via Gaussian Splatting
This work addresses time-consuming and resource-intensive flame analysis for combustion diagnostics, though it appears incremental as it builds on flame simulation technology.
The paper tackles the computational inefficiency of traditional ART algorithms for flame combustion diagnosis by proposing a novel flame representation method, achieving an average SSIM of 0.96, PSNR of 39.05, and saving about 34 times computation time and 10 times memory.
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.