Proceedings Paper3D-model localization using high-resolution reconstruction of monocular image sequences
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In this paper, we present a complete system for the recognition and localization of a 3D model from a sequence of monocular images with known motion. The originality of this system is twofold. First, it uses a purely 3D approach, starting from the 3D reconstruction of the scene and ending by the 3D matching of the model. Second, unlike most monocular systems, we do not use token tracking to match successive images. Rather, subpixel contour matching is used to recover more precisely complete 3D contours, yielding a denser and higher level representation of the scene. The reconstructed contours are fused along successive images to further increase the localization precision and the robustness of the 3D reconstruction. Finally, corners are extracted from the 3D contours and used to generate hypotheses of the model position in a hypothesize-and-verify algorithm. This algorithm yields a robust recognition and precise localization of the model in the scene. Results are presented on infrared image sequences with different resolutions, demonstrating the precision of the localization as well as the robustness and the low computational complexity of the algorithms.