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In this project, we are interested in the fundamental problem of simultaneously tracking a piecewise-planar scene and accurately estimating the 3D displacement of the camera. The objective is to have an algorithm with a higher convergence frequency and a higher convergence rate than standard optimization methods while having an equivalent computational complexity. Since the tracking is aimed to be used in industrial real-time augmented reality applications, in addition to the robustness and the speed performance, the algorithm should be very precise.
For some applications, we are investigating alternatives where an off-line learning step improves the efficiency and the convergence properties of the tracking algorithm.
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