从稀疏视频重建两相流界面动态，实现高精度三维建模与速度估计。
SurfPhase: 3D Interfacial Dynamics in Two-Phase Flows from Sparse Videos
- 结合动态高斯面元与符号距离函数，保证几何一致性。
- 仅用两视角视频即可实现高质量视角合成与速度估计。
- 适用于高速沸腾等复杂两相流研究，适合流体模拟与工程仿真人员。
两相流中的界面动力学主导动量、热量和质量传递，但实验测量困难。传统方法在移动界面附近存在固有局限，现有神经渲染方法针对单相流且边界模糊，无法处理尖锐可变形的液-汽界面。我们提出SurfPhase，一种从稀疏相机视角重建3D界面动态的新模型。该方法融合动态高斯面元与符号距离函数以保证几何一致性，并利用视频扩散模型生成新视角视频，从而从稀疏观测中优化重建结果。我们在新构建的高速池沸腾视频数据集上进行评估，仅用两个相机视角即实现了高质量视角合成与速度估计。
原文摘要 · Abstract (English)
Interfacial dynamics in two-phase flows govern momentum, heat, and mass transfer, yet remain difficult to measure experimentally. Classical techniques face intrinsic limitations near moving interfaces, while existing neural rendering methods target single-phase flows with diffuse boundaries and cannot handle sharp, deformable liquid-vapor interfaces. We propose SurfPhase, a novel model for reconstructing 3D interfacial dynamics from sparse camera views. Our approach integrates dynamic Gaussian surfels with a signed distance function formulation for geometric consistency, and leverages a video diffusion model to synthesize novel-view videos to refine reconstruction from sparse observations. We evaluate on a new dataset of high-speed pool boiling videos, demonstrating high-quality view synthesis and velocity estimation from only two camera views. Project website: https://yuegao.me/SurfPhase.
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