Single-View Shape and Depth from Closed-form Polarization Models for Depolarization-Dominated Objects
Affiliation
Wyant College of Optical Sciences, University of ArizonaIssue Date
2023-10-03
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SPIECitation
Quinn Jarecki, Meredith Kupinski, "Single-view shape and depth from closed-form polarization models for depolarization-dominated objects," Proc. SPIE 12690, Polarization Science and Remote Sensing XI, 1269004 (3 October 2023); https://doi.org/10.1117/12.2676996Rights
© 2023 SPIE. (2023) Published by SPIE.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
Closed-form solutions for shape-from-polarization (SfP) generally assume either purely specular or purely diffuse polarized light scattering models. However, polarized light scattering from real-world objects is a mixture of both of these processes. This work makes use of a closed-form expression for polarized light scattering model which combines specular and diffuse contributions. In prior work, we have demonstrated the broad applicability of a triply-degenerate (TD) model which decouples depolarization from the dominant Mueller-Jones matrix (MJM). The depolarization is controlled by a single parameter and the MJM encodes the polarization-dependent properties (e.g. diattenuation, polarizance). In this work, SfP information content is explored using our model for the MJM term which combines diffuse and specular polarization to simulate single-view, noise-free Mueller images. A merit function for simultaneous estimates of per-pixel surface normal and absolute depth is proposed. Cross-sections of this merit function are shown to be convex along depth and contain erroneous ambiguities for the surface normal. While ambiguities in surface normal estimates are well known for existing SfP approaches, these cross-sections show a kind of ambiguity unique to our model. Through investigation of the idealized scenario of an exactly-known pBRDF model and noise-free, infinitely precise polarimetric measurements, we found that simultaneous depth and shape estimation is achievable. © 2023 SPIE.Note
Immediate accessISSN
0277-786XISBN
978-151066594-1Version
Final Published Versionae974a485f413a2113503eed53cd6c53
10.1117/12.2676996