Item

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 and is already in use for production vehicles. Despite the enormous progress in the field and the high performance of modern methods, one key challenge remains: stereo vision in automotive scenarios during difficult weather or illumination conditions. Current methods generate strong temporal noise,�many disparity outliers, and false positives on a segmentation level. This work addresses these issues by formulating a temporal prior and a scene prior and applying them to SGM. For image sequences captured on a highway during rain, during snowfall, or in low light, these priors significantly improve the object detection rate while reducing the false positive rate. The algorithm also outperforms the�ECCV Robust�Vision Challenge winner, iSGM.�