Priors for Stereo Vision Under Adverse Weather Conditions

Abstract

Autonomous Driving benefits strongly from a 3D reconstruction of the environment in real-time, often obtained via stereo vision. Semi-Global Matching (SGM) is a popular method of choice for solving this task which is already in use for production vehicles. Despite the enormous progress in the field and the high level of performance of modern methods, one key challenge remains: stereo vision in automotive scenarios during weather conditions such as rain, snow and night. Current methods generate strong temporal noise, many disparity outliers and false positives on a segmentation level. They are addressed in this work. We formulate a temporal prior and a scene prior, which we apply to SGM and Graph Cut. Using these priors, the object detection rate improves significantly on a driver assistance database of 3000 frames including bad weather while reducing the false positive rate. We also outperform the ECCV Robust Vision Challenge winner, iSGM, on this database.