Photoelasticity enables full-field stress analysis in transparent objects through stress-induced birefringence. Existing techniques are limited to 2D slices and require destructively slicing the object. Recovering the internal 3D stress distribution of the entire object is challenging as it involves solving a tensor tomography problem and handling phase wrapping ambiguities. We introduce NeST, an analysis-by-synthesis approach for reconstructing 3D stress tensor fields as neural implicit representations from polarization measurements. Our key insight is to jointly handle phase unwrapping and tensor tomography using a differentiable forward model based on Jones calculus. Our non-linear model faithfully matches real captures, unlike prior linear approximations. We develop an experimental multi-axis polariscope setup to capture 3D photoelasticity and experimentally demonstrate that NeST reconstructs the internal stress distribution for objects with varying shape and force conditions. Additionally, we showcase novel applications in stress analysis, such as visualizing photoelastic fringes by virtually slicing the object and viewing photoelastic fringes from unseen viewpoints. NeST paves the way for scalable non-destructive 3D photoelastic analysis.

通过应力诱导双折射, 光弹性能使透明物体的压力分析变得全场，但现有技术只限于二维切片且需要对物体进行破坏性切割。我们引入了NeST，这是一种通过极化测量从神经隐式表示重建三维应力张量场的分析合成方法。我们的关键见解是使用基于琼斯计算的可微分正向模型共同处理相位展开和张量层析成像。我们开发了一个实验性的多轴偏振仪设置来捕捉三维光弹性，并在实验中展示了NeST对具有不同形状和受力条件的物体内部应力分布的重建。此外，我们展示了应力分析中的新颖应用，例如通过虚拟切割物体来可视化光弹性条纹并从未见过的视角观察光弹性条纹。NeST为可扩展的非破坏性三维光弹性分析铺平了道路。

NeST: 利用三维光弹性进行神经应力张量层析成像