@inproceedings {pub2874,
	title = {A Multi Block-Matching Approach for Stereo},
	author = {Nils Einecke AND Julian Eggert},
	year = {2015},
	month = {June},
	abstract = {Block-Matching stereo is commonly used in applications with minimal computing resources in order to get some rough depth estimates. However, research on this simple stereo estimation technique has been very low since the advent of energy-based methods which promise higher quality and a larger creational research freedom. In the domain of intelligent vehicles, especially semi-global-matching (SGM) is widely spread due to its good performance. In this paper, we will introduce a novel multi-block-matching scheme which tremendously improves the result of block-matching stereo while keeping a low memory-footprint and a low computational complexity. We tested our new multi-block-matching scheme on the KITTI stereo benchmark as well as on the new Middlebury stereo benchmark. For the KITTI benchmark we achieve results that even surpass the results of the best SGM implementations. For the new Middlebury benchmark we get results that are slightly worse than state-of-the-art SGM implementations.},
	publisher = {IEEE},
	booktitle = {Intelligent Vehicles Symposium},
	pages = {585--592}
}