用高斯表示法从三视角重建动态火焰，捕捉高频细节。
Gaussians on Fire: High-Frequency Reconstruction of Flames
- 用高斯构建时空表示，分离背景与火焰区域。
- 通过光流融合初始化3D火焰场，支持亚帧级时间对齐。
- 仅需三视角和普通硬件，适合真实复杂火场重建。
我们提出一种基于高斯的时空表示方法，仅需三个相机视角即可实现动态火焰的三维重建。由于火焰具有易变性、透明性和丰富的高频特征，其捕获与重建极具挑战。为应对欠定几何问题，我们结合密集多视角立体视觉与单目深度先验，将静态背景与动态火焰区域分离。火焰初始为3D流场，由各视角密集光流投影融合生成。为捕捉火焰高频特征，每个3D高斯编码寿命与线性速度以匹配密集光流。为实现跨相机亚帧级时间对齐，采用定制硬件同步模式，可在普通商用硬件上完成重建。在多种真实火焰场景下的定量与定性验证表明，该方法在复杂环境下仍具鲁棒性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。