Today's stereo vision algorithms and computing technology allow real-time 3D data analysis, for example for driver assistance systems. A recently developed Semi-Global Matching (SGM) approach by H. Hirschmuller became a popular choice due to performance and robustness. This paper evaluates different parameter settings for SGM, and its main contribution consists in suggesting to include a second order prior into the smoothness term of the energy function. It also proposes and tests a new cost function for SGM. Furthermore, some preprocessing (edge images) proved to be of great value for improving SGM stereo results on real-world sequences, as previously already shown by S. Guan and R. Klette for belief propagation. There is also a performance gain for engineered stereo data (e.g.) as currently used on the Middlebury stereo website. However, the fact that results are not as impressive as on the .enpeda.. sequences indicates that optimizing for engineered data does not neccessarily improve real world stereo data analysis.
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