Towards urban scenes understanding through polarization cues
This work addresses the challenge of specular phenomena for autonomous robots, offering a domain-specific improvement in scene understanding.
The paper tackled the problem of robust scene understanding for autonomous robotics in dynamic urban environments by incorporating polarization sensing to handle specular obstacles, resulting in improved segmentation and depth estimation compared to RGB-centric methods.
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.