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Robust Dense Matching Using Local and Global Geometric Constraints

Maxime Lhuillier 1 Long Quan 1
1 MOVI - Modeling, localization, recognition and interpretation in computer vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : A new robust dense matching algorithm is introduced. The algorithm starts from matching the most textured points, then a match propagation algorithm is developed with the best first strategy to dense matching. Next, the matching map is regularised by using the local geometric constraints encoded by planar affine applications and by using the global geometric constraint encoded by the fundamental matrix. Two most distinctive features are a match propagation strategy developed by analogy to region growing and a successive regularisation by local and global geometric constraints. The algorithm is efficient, robust and can cope with wide disparity. The algorithm is demonstrated on many real image pairs, and applications on image interpolation and a creation of novel views are also presented.
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Maxime Lhuillier, Long Quan. Robust Dense Matching Using Local and Global Geometric Constraints. 15th International Conference on Pattern Recognition (ICPR '00), Sep 2000, Barcelona, Spain. pp.968--972, ⟨10.1109/ICPR.2000.905620⟩. ⟨inria-00590140⟩



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