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P2D: a self-supervised method for depth estimation from polarimetry

Marc Blanchon 1 Désiré Sidibé 2 Olivier Morel 1 Ralph Seulin 1 Daniel Braun 1 Fabrice Meriaudeau 1 
1 VIBOT - Equipe VIBOT - VIsion pour la roBOTique [ImViA EA7535 - ERL CNRS 6000]
CNRS - Centre National de la Recherche Scientifique : ERL 6000, ImViA - Imagerie et Vision Artificielle [Dijon]
Abstract : Monocular depth estimation is a recurring subject in the field of computer vision. Its ability to describe scenes via a depth map while reducing the constraints related to the formulation of perspective geometry tends to favor its use. However, despite the constant improvement of algorithms, most methods exploit only colorimetric information. Consequently, robustness to events to which the modality is not sensitive to, like specularity or transparency, is neglected. In response to this phenomenon, we propose using polarimetry as an input for a self-supervised monodepth network. Therefore, we propose exploiting polarization cues to encourage accurate reconstruction of scenes. Furthermore, we include a term of polarimetric regularization to state-of-the-art method to take specific advantage of the data. Our method is evaluated both qualitatively and quantitatively demonstrating that the contribution of this new information as well as an enhanced loss function improves depth estimation results, especially for specular areas.
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Submitted on : Monday, October 26, 2020 - 9:56:35 AM
Last modification on : Thursday, August 4, 2022 - 5:07:03 PM
Long-term archiving on: : Wednesday, January 27, 2021 - 6:26:53 PM


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  • HAL Id : hal-02977824, version 1


Marc Blanchon, Désiré Sidibé, Olivier Morel, Ralph Seulin, Daniel Braun, et al.. P2D: a self-supervised method for depth estimation from polarimetry. 25th International Conference on Pattern Recognition (ICPR 2020), Jan 2021, Milan, Italy. ⟨hal-02977824⟩



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