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@inproceedings{Deschaintre:2021:10.1109/CVPR46437.2021.01531,
author = {Deschaintre, V and Lin, Y and Ghosh, A},
doi = {10.1109/CVPR46437.2021.01531},
pages = {15562--15571},
publisher = {IEEE},
title = {Deep polarization imaging for 3D shape and SVBRDF acquisition},
url = {http://dx.doi.org/10.1109/CVPR46437.2021.01531},
year = {2021}
}

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TY  - CPAPER
AB  - We  present  a  novel  method  for  efficient  acquisition  of shape and spatially varying reflectance of 3D objects using polarization cues.  Unlike previous works that have exploited polarization to estimate material or object appearance under certain constraints (known shape or multiview acquisition), we lift such restrictions by coupling polarization  imaging  with  deep  learning  to  achieve  high  quality estimate of 3D  object shape (surface normals and  depth)and SVBRDF using single-view polarization imaging under frontal flash illumination. In addition to acquired polarization images, we provide our deep network with strong novel cues related to shape and reflectance, in the form of a normalized Stokes map and an estimate of diffuse color.   We additionally describe modifications to network architecture and training loss which provide further qualitative improvements.  We demonstrate our approach to achieve superior results compared to recent works employing deep learning in conjunction with flash illumination.
AU  - Deschaintre,V
AU  - Lin,Y
AU  - Ghosh,A
DO  - 10.1109/CVPR46437.2021.01531
EP  - 15571
PB  - IEEE
PY  - 2021///
SP  - 15562
TI  - Deep polarization imaging for 3D shape and SVBRDF acquisition
UR  - http://dx.doi.org/10.1109/CVPR46437.2021.01531
UR  - http://hdl.handle.net/10044/1/89191
ER  -

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ProfessorAbhijeetGhosh

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