Depth Sensing Using Geometrically Constrained Polarization Normals
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11263_2017_1025_ReferencePDF.pdf
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Author(s)
Kadambi, Achuta
Taamazyan, Vage Aramaisovich
Shi, Boxin
Raskar, Ramesh
Date Issued
June 2017
Journal
International Journal of Computer Vision
Publisher
Springer US
Citation
Kadambi, Achuta, et al. “Depth Sensing Using Geometrically Constrained Polarization Normals.” International Journal of Computer Vision, vol. 125, no. 1–3, Dec. 2017, pp. 34–51.
Version
Author's final manuscript
Abstract
Analyzing the polarimetric properties of reflected light is a potential source of shape information. However, it is well-known that polarimetric information contains fundamental shape ambiguities, leading to an underconstrained problem of recovering 3D geometry. To address this problem, we use additional geometric information, from coarse depth maps, to constrain the shape information from polarization cues. Our main contribution is a framework that combines surface normals from polarization (hereafter polarization normals) with an aligned depth map. The additional geometric constraints are used to mitigate physics-based artifacts, such as azimuthal ambiguity, refractive distortion and fronto-parallel signal degradation. We believe our work may have practical implications for optical engineering, demonstrating a new option for state-of-the-art 3D reconstruction. Keywords: Computational photography, Light transport, Depth sensing, Shape from polarization
MIT Department
Massachusetts Institute of Technology. Media Laboratory
MIT Skoltech Program
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/s11263-017-1025-7
