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.
['Maxime Lhuillier', 'Long Quan']
Robust dense matching using local and global geometric constraints