Polarimetric Imaging for Perception
Michael Baltaxe Tomer Pe'er Dan Levi
Autonomous driving and advanced driver-assistance systems rely on a set of
sensors and algorithms to perform the appropriate actions and provide alerts as
a function of the driving scene. Typically, the sensors include color cameras,
radar, lidar and ultrasonic sensors. Strikingly however, although light
polarization is a fundamental property of light, it is seldom harnessed for
perception tasks. In this work we analyze the potential for improvement in
perception tasks when using an RGB-polarimetric camera, as compared to an RGB
camera. We examine monocular depth estimation and free space detection during
the middle of the day, when polarization is independent of subject heading, and
show that a quantifiable improvement can be achieved for both of them using
state-of-the-art deep neural networks, with a minimum of architectural changes.
We also present a new dataset composed of RGB-polarimetric images, lidar scans,
GNSS / IMU readings and free space segmentations that further supports
developing perception algorithms that take advantage of light polarization.