用高斯点云重建火焰光场，速度提升34倍且精度高
FlameGS: Reconstruct flame light field via Gaussian Splatting
- 基于火焰发光过程建模，用2D投影图监督训练
- 预测图像与真实图像结构相似度达0.96，峰值信噪比39.05
- 相比传统方法提速34倍、内存降低10倍，适合实时诊断
为解决传统ART算法在火焰燃烧诊断中耗时长、计算量大的问题，受火焰模拟技术启发，我们提出一种新型火焰表示方法。通过建模火焰发光过程，并利用2D投影图像进行监督，实验验证表明，该模型在实际图像与预测2D投影之间实现了平均结构相似度0.96和峰值信噪比39.05。同时，相较于传统算法，计算时间减少约34倍，内存占用降低约10倍。
原文摘要 · Abstract (English)
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.
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