Gaussians on Fire: High-Frequency Reconstruction of Flames

We propose a method to reconstruct dynamic fire in 3D froma limited set of camera views with a Gaussian-based spatiotemporal rep-resentation. Capturing and reconstructing fire and its dynamics is highlychallenging due to its volatile nature, transparent quality, and multitudeof high-frequency features. Despite these challenges, we aim to recon-struct fire from only three views, which consequently requires solvingfor under-constrained geometry. We solve this by separating the staticbackground from the dynamic fire region by combining dense multi-viewstereo images with monocular depth priors. The fire is initialized as a 3Dflow field, obtained by fusing per-view dense optical flow projections. Tocapture the high-frequency features of fire, each 3D Gaussian encodesa lifetime and linear velocity to match the dense optical flow. To en-sure sub-frame temporal alignment across cameras, we employ a customhardware synchronization pattern – allowing us to reconstruct fire withaffordable commodity hardware. Our quantitative and qualitative vali-dations across numerous reconstruction experiments demonstrate robustperformance for diverse and challenging real and simulated fire scenarios.