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We present a novel approach that, given two sets of unmatched keypoints, simultaneously estimates the in-plane camera motion and keypoint matches without using photometric information. Standard approaches estimate the epipolar geometry based on putative matches, first established with photometric information, then accepted or rejected using the epipolar constraint. Our method discretizes the space of essential matrices at different levels. It searches for the essential matrix and keypoint matches which are the most geometrically coherent. We maximize geometric coherence, that we define as the number of points that can be matched based on the epipolar and unicity constraints. We applied this general framework to sets of images acquired by a moving tripod. We present promising results on simulated and real data.