  Autonomous robotics is critically affected by the robustness of its scene
understanding algorithms. We propose a two-axis pipeline based on polarization
indices to analyze dynamic urban scenes. As robots evolve in unknown
environments, they are prone to encountering specular obstacles. Usually,
specular phenomena are rarely taken into account by algorithms which causes
misinterpretations and erroneous estimates. By exploiting all the light
properties, systems can greatly increase their robustness to events. In
addition to the conventional photometric characteristics, we propose to include
polarization sensing.
  We demonstrate in this paper that the contribution of polarization
measurement increases both the performances of segmentation and the quality of
depth estimation. Our polarimetry-based approaches are compared here with other
state-of-the-art RGB-centric methods showing interest of using polarization
imaging.
