A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
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Autonomous Robots
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The accuracy of stereo algorithms is commonly assessed by comparing the results against the Middlebury database. However, no equivalent data for automotive or robotics applications exist and these are difficult to obtain. We introduce a performance evaluation scheme and metrics for stereo algorithms at three different levels. This evaluation can be reproduced with comparably low effort and has very few prerequisites. First, the disparity images are evaluated on a pixel level. The second level evaluates the disparity data roughly column by column, and the third level performs an evaluation on an object level. We compare three real-time capable stereo algorithms with these methods and the results show that a global stereo method, semi-global matching, yields the best performance using our metrics that incorporate both accuracy and robustness.