Modeling the mechanics of fluid in complex scenes is vital to applications in
design, graphics, and robotics. learning-based methods provide fast and
differentiable fluid simulators, however most prior work is unable to
accurately model how fluids interact with genuinely novel surfaces
本文介绍一种基于 Deep Signed Distance Function 的不可微分网格表述方法 MeshSDF，通过推理隐式场的扰动如何影响局部表面几何，最终不限制分辨率和拓扑结构地将 Deep Implicit Field 显式地表示成一个可微分的网格形状，该方法在单视角重建和基于物理的形状优化方面优于现有技术。