Depth from stereo polarization in specular scenes for urban robotics


3-D perception of scenes with specular surfaces is still challenging for robotics applications in urban areas, for both active and passive range sensors; there is a need for improved solutions that work without artificial illumination over a wide range of distances. The advent of cameras with microgrid polarization filter arrays, which allow acquiring four orientations of linearly polarized images simultaneously, has potential to make the use of polarization information in 3-D perception more practical. It is well-known that polarization can provide information about the orientation of specular surfaces; however, prior work with polarization for 3-D perception has had several limitations. We present the first unified formulation of depth perception with stereo and polarization by extending previous energy minimization formulations to include surface orientation constraints computed from the polarization channels. We apply an existing quadratic pseudo-boolean optimization (QPBO) method to approximate the optimal depth map. We use synthetic and real indoor/outdoor images to demonstrate that the new method achieves better results than prior methods, with fewer assumptions and limitations.

IEEE International Conference on Robotics and Automation (ICRA)