Physics-Guided Deep Learning for Linear Mueller Matrix Acquisition

The Mueller matrix encodes rich structural and physical information, but its rapid and accurate estimation remains challenging. While physics-based polarimetric Bidirectional Reflectance Distribution Function (pBRDF) models provide useful formulation constraints, their application is limited by inaccessible parameters and model mismatch. To address this challenge, we propose a physics-guided two-stage framework for single-shot linear Mueller matrix recovery. Given one set of four polarization images and an object mask under a fixed known incident polarization state, the first stage estimates pBRDF-related parameters and uses an analytical pBRDF model to construct a structured Mueller initialization. A second network then refines this initialization through residual correction. Evaluated on a newly constructed hybrid real-synthetic dataset, our method improves matrix reconstruction, observed-state rendering, held-out forward Stokes prediction, and physical-plausibility diagnostics over adapted pBRDF fitting baselines. Downstream validation through shape from polarization (SfP) and material classification further provides secondary evidence that the recovered matrices preserve useful polarimetric cues for geometric and material analysis.