PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification
PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification
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DOI：
10.48550/arxiv.2303.05512
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发表时间：
2023-03
期刊：
影响因子：
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通讯作者：
Xuan Li;Yi-Ling Qiao;Peter Yichen Chen;Krishna Murthy Jatavallabhula;Ming Lin;Chenfanfu Jiang;Chuang Gan
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文献类型：
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作者：
Xuan Li;Yi-Ling Qiao;Peter Yichen Chen;Krishna Murthy Jatavallabhula;Ming Lin;Chenfanfu Jiang;Chuang Gan
现有的方法系统识别（估计对象的物理参数）从视频假设已知的对象几何形状。这排除了它们在对象几何形状复杂或未知的绝大多数场景中的适用性。在这项工作中，我们的目标是从一组多视图视频中识别表征物理系统的参数，而无需对对象的几何形状或拓扑结构进行任何假设。为此，我们提出了“物理增强连续神经辐射场”（PAC-NeRF），从多视图视频中估计高度动态对象的未知几何形状和物理参数。我们设计的PAC-NeRF只有通过强制神经辐射场遵循连续介质力学的守恒定律来产生物理上合理的状态。为此，我们设计了神经辐射场的混合欧拉-拉格朗日表示，即，我们使用NeRF密度和颜色场的欧拉网格表示，同时通过拉格朗日粒子平流输送神经辐射场。这种混合欧拉-拉格朗日表示无缝地将高效的神经渲染与材料点方法（MPM）融合在一起，以实现鲁棒的可微物理模拟。我们验证了我们提出的框架在广泛的材料，包括弹性体，橡皮泥，沙子，牛顿和非牛顿流体的几何形状和物理参数估计的有效性，并在大多数任务上表现出显着的性能增益。
Existing approaches to system identification (estimating the physical parameters of an object) from videos assume known object geometries. This precludes their applicability in a vast majority of scenes where object geometries are complex or unknown. In this work, we aim to identify parameters characterizing a physical system from a set of multi-view videos without any assumption on object geometry or topology. To this end, we propose"Physics Augmented Continuum Neural Radiance Fields"(PAC-NeRF), to estimate both the unknown geometry and physical parameters of highly dynamic objects from multi-view videos. We design PAC-NeRF to only ever produce physically plausible states by enforcing the neural radiance field to follow the conservation laws of continuum mechanics. For this, we design a hybrid Eulerian-Lagrangian representation of the neural radiance field, i.e., we use the Eulerian grid representation for NeRF density and color fields, while advecting the neural radiance fields via Lagrangian particles. This hybrid Eulerian-Lagrangian representation seamlessly blends efficient neural rendering with the material point method (MPM) for robust differentiable physics simulation. We validate the effectiveness of our proposed framework on geometry and physical parameter estimation over a vast range of materials, including elastic bodies, plasticine, sand, Newtonian and non-Newtonian fluids, and demonstrate significant performance gain on most tasks.