  Recently, works on improving the naturalness of stitching images gain more
and more extensive attention. Previous methods suffer the failures of severe
projective distortion and unnatural rotation, especially when the number of
involved images is large or images cover a very wide field of view. In this
paper, we propose a novel natural image stitching method, which takes into
account the guidance of vanishing points to tackle the mentioned failures.
Inspired by a vital observation that mutually orthogonal vanishing points in
Manhattan world can provide really useful orientation clues, we design a scheme
to effectively estimate prior of image similarity. Given such estimated prior
as global similarity constraints, we feed it into a popular mesh deformation
framework to achieve impressive natural stitching performances. Compared with
other existing methods, including APAP, SPHP, AANAP, and GSP, our method
achieves state-of-the-art performance in both quantitative and qualitative
experiments on natural image stitching.
