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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2511.22459 |
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| _version_ | 1866912734417780736 |
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| author | Nazarenus, Jakob Michels, Dominik Palubicki, Wojtek Kou, Simin Zhang, Fang-Lue Pirk, Soren Koch, Reinhard |
| author_facet | Nazarenus, Jakob Michels, Dominik Palubicki, Wojtek Kou, Simin Zhang, Fang-Lue Pirk, Soren Koch, Reinhard |
| contents | 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 fire scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_22459 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Gaussians on Fire: High-Frequency Reconstruction of Flames Nazarenus, Jakob Michels, Dominik Palubicki, Wojtek Kou, Simin Zhang, Fang-Lue Pirk, Soren Koch, Reinhard Computer Vision and Pattern Recognition 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 fire scenarios. |
| title | Gaussians on Fire: High-Frequency Reconstruction of Flames |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.22459 |