[Submitted on 14 Jul 2026]
Title:Differentiable Polarized Path Tracing
View a PDF of the paper titled Differentiable Polarized Path Tracing, by Pramod Rao and 8 other authors
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Abstract:Physically based differentiable rendering has proven to be a powerful tool for inverse rendering problems (e.g., 3D reconstruction, reflectance estimation, lighting estimation). However, most existing methods operate solely on radiometric intensity, discarding valuable polarization cues that constrain scene geometry and material properties. While forward simulation of polarized light is well-defined via Mueller-Stokes calculus, extending reverse-mode differentiation to this domain presents significant challenges. The rank-deficient nature of common polarimetric operators, such as linear polarizers and diffuse reflections, violates the invertibility assumptions of standard gradient estimators like path replay backpropagation and results in numerical instability. We address this by proposing a robust, polarization-aware differentiable path tracing method. Our approach estimates unbiased gradients through a combination of path replay and local caching. This formulation enables efficient and stable optimization of material and lighting parameters in complex scenes, broadening the applicability of physically based inverse rendering. Project page: this https URL
Comments: Accepted at ECCV 2026
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.13265 [cs.CV]
(or arXiv:2607.13265v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.13265
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Pramod Rao [view email] [v1] Tue, 14 Jul 2026 20:58:08 UTC (33,449 KB)
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