Image matching for 3D reconstruction using complementary optical and geometric information

Abstract : Abstract Image matching is a central research topic in computer vision which has been mainly focused on optical aspects. The aim of the work presented herein consists in the direct use of geometry to complement optical information in the tasks of 2D matching. First, we focus on global methods based on the calculus of variations. In such methods occlusions and sharp features raise difficult challenges. In these scenarios only the contribution of the regularizer accounts for results. Based on a geometric characterization of this behaviour, we formulate a variational matching method that steers grid lines away from problematic regions. While variational methods provide well behaved results, local methods based on match propagation provide results that adapt closely to varying 3D structures although choppy in nature. Therefore, we present a novel method to propagate matches using local information about surface regularity correcting 3D positions along with corresponding 2D matchings
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Patricio A. Galindo. Image matching for 3D reconstruction using complementary optical and geometric information. Other [cs.OH]. Université de Lorraine, 2015. English. ⟨NNT : 2015LORR0007⟩. ⟨tel-01751407⟩

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