Polarimetric Imaging for Perception

• URL: http://arxiv.org/abs/2305.14787v1
• Date: Wed, 24 May 2023 06:42:27 GMT
• Title: Polarimetric Imaging for Perception
• Authors: Michael Baltaxe, Tomer Pe'er, Dan Levi
• Abstract summary: We analyze the potential for improvement in perception tasks when using an RGB-polarimetric camera. We show that a quantifiable improvement can be achieved for both of them using state-of-the-art deep neural networks.
• Score: 3.093890460224435
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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