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Performance analysis of stereo reconstruction algorithms

Environment mapping is one of the most critical tasks in the development of driving assistance systems and stereo vision has been widely used to accomplish it. However, there are very few datasets that allow assessing the performance of a specific method in a real world application. Most datasets have been created in controlled conditions, thus neglecting scenarios that are impossible to reproduce in a laboratory. In this paper, we present the results of the evaluation of three different dense reconstruction algorithm implementations using a number of well-known strategies that represent different trade-offs in terms of cost, set up time and accuracy. In our tests, we evaluated two variants of the Semi-Global Matching algorithm, and the Efficient Large-Scale Stereo Matching method, as well as different combinations of additional filters in order to assess their influence on the final behavior of the algorithms.