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

TL;DR

This paper introduces P2D, a self-supervised depth estimation method using polarimetry data, which improves accuracy in challenging regions like specular surfaces by leveraging polarization cues and regularization.

Contribution

It is the first to incorporate polarimetric information into self-supervised monocular depth estimation, enhancing robustness and accuracy over traditional color-only methods.

Findings

Improved depth estimation in specular regions.

Enhanced reconstruction accuracy with polarimetric regularization.

Quantitative and qualitative validation shows superior performance.

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.