P2D: a self-supervised method for depth estimation from polarimetry
This work addresses robustness issues in depth estimation for computer vision applications, but it is incremental as it builds on existing self-supervised methods by adding polarimetric data and regularization.
The authors tackled the problem of monocular depth estimation's sensitivity to specular and transparent surfaces by introducing a self-supervised method that uses polarimetry as input, resulting in improved depth estimation, particularly for specular areas.
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.