Quasi-Homography Warps in Image Stitching

The naturalness of warps is gaining extensive attention in image stitching. Recent warps, such as SPHP and AANAP, use global similarity warps to mitigate projective distortion (which enlarges regions); however, they necessarily bring in perspective distortion (which generates inconsistencies). In this paper, we propose a novel quasi-homography warp, which effectively balances the perspective distortion against the projective distortion in the non-overlapping region to create a more natural-looking panorama. Our approach formulates the warp as the solution of a bivariate system, where perspective distortion and projective distortion are characterized as slope preservation and scale linearization, respectively. Because our proposed warp only relies on a global homography, it is thus totally parameter free. A comprehensive experiment shows that a quasi-homography warp outperforms some state-of-the-art warps in urban scenes, including homography, AutoStitch and SPHP. A user study demonstrates that it wins most users' favor, compared to homography and SPHP.