  Stitched images provide a wide field-of-view (FoV) but suffer from unpleasant
irregular boundaries. To deal with this problem, existing image rectangling
methods devote to searching an initial mesh and optimizing a target mesh to
form the mesh deformation in two stages. Then rectangular images can be
generated by warping stitched images. However, these solutions only work for
images with rich linear structures, leading to noticeable distortions for
portraits and landscapes with non-linear objects. In this paper, we address
these issues by proposing the first deep learning solution to image
rectangling. Concretely, we predefine a rigid target mesh and only estimate an
initial mesh to form the mesh deformation, contributing to a compact one-stage
solution. The initial mesh is predicted using a fully convolutional network
with a residual progressive regression strategy. To obtain results with high
content fidelity, a comprehensive objective function is proposed to
simultaneously encourage the boundary rectangular, mesh shape-preserving, and
content perceptually natural. Besides, we build the first image stitching
rectangling dataset with a large diversity in irregular boundaries and scenes.
Experiments demonstrate our superiority over traditional methods both
quantitatively and qualitatively.
