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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.�
