An automatized method to parameterize embedded stereo matching algorithms

International audienceMany applications rely on 3D information as a depth map. Stereo Matching algorithms reconstruct a depth map from a pair of stereoscopic images. Stereo Matching algorithms are computationally intensive, that is why implementing efficient stereo matching algorithms on embedded systems is very challenging for real-time applications. Indeed, like many vision algorithms, stereo matching algorithms have to set a lot of parameters and thresholds to work efficiently. When optimizing a stereo-matching algorithm, or changing algorithms parts, all those parameters have to be set manually. Finding the most efficient solution for a stereo-matching algorithm on a specific platform then becomes troublesome. This paper proposes an automatized method to find the optimal parameters of a dense stereo matching algorithm by learning from ground truth on a database in order to compare it with respect to any other alternative. Finally, for the C6678 platform, a map of the best compromise between quality and execution time is obtained, with execution times that are between 42 ms and 382 ms and output errors that are between 6% and 9.8%