Robust dense matching using local and global geometric constraints

A new robust dense matching algorithm is introduced in this paper. The algorithm starts from matching the most textured points, then a match propagation algorithm is developed with the best first strategy to densify the matches. 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 creating novel views are also presented.