NeST: Neural Stress Tensor Tomography by leveraging 3D Photoelasticity

TL;DR

NeST introduces a neural implicit approach to reconstruct 3D stress tensor fields in transparent objects from polarization measurements, overcoming limitations of existing 2D methods and enabling non-destructive internal stress analysis.

Contribution

It presents a novel analysis-by-synthesis neural method that jointly handles phase unwrapping and tensor tomography for 3D stress reconstruction from photoelasticity data.

Findings

Successfully reconstructs 3D stress fields in various objects

Demonstrates visualization of photoelastic fringes from new viewpoints

Validates the approach with experimental data

Abstract

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.