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.

本文提出了一种基于极化成像的自监督单目深度学习网络，以利用色散不敏感的极化信息，提高深度估计结果，特别是对于高光区域。