Modeling arbitrarily oriented slanted planes for efficient stereo vision based on block matching

Stereo cameras enable a 3D reconstruction of viewed scenes and are therefore well-suited sensors for many advanced driver assistance systems and autonomous driving. Modern algorithms for estimating distances for every image pixel achieve high-quality results, but their real-time capability is very limited. In contrast, window-based local methods can be implemented very efficiently but are more prone to errors. This is particularly true for spatial changes of distance within the matching window, most prominently on surfaces such as the road which are not parallel to but rather slanted towards the image plane. In this paper we present a method to compensate the impact of this effect for arbitrarily oriented sets of planes. It does not depend on any modifications to the actual distance estimation. Instead, it only applies specific transformations to input images and intermediate results. By combining this approach with existing implementations which efficiently use either multi-core or graphics processors, we were able to significantly increase quality while maintaining real-time throughputs on a compact target system.