arXiv:2511.22459 · v3
Gaussians on Fire: High-Frequency Reconstruction of Flames
摘要
We propose a method to reconstruct dynamic fire in 3D from a limited set of camera views with a Gaussian-based spatiotemporal representation. Capturing and reconstructing fire and its dynamics is highly challenging due to its volatile nature, transparent quality, and multitude of high-frequency features. Despite these challenges, we aim to reconstruct fire from only three views, which consequently requires solving for under-constrained geometry. We solve this by separating the static background from the dynamic fire region by combining dense multi-view stereo images with monocular depth priors. The fire is initialized as a 3D flow field, obtained by fusing per-view dense optical flow projections. To capture the high-frequency features of fire, each 3D Gaussian encodes a lifetime and linear velocity to match the dense optical flow. To ensure sub-frame temporal alignment across cameras, we employ a custom hardware synchronization pattern -- allowing us to reconstruct fire with affordable commodity hardware. Our quantitative and qualitative validations across numerous reconstruction experiments demonstrate robust performance for diverse and challenging real and simulated fire scenarios.
作者备注：38 pages, 17 figures, 8 tables; ECCV 2026 version with corrected results (bugfix in the initialization of our method for real-world scenes)
期刊信息：Computer Vision -- ECCV 2026, LNCS vol. 17068, pp. 454-476, Springer (2026)