  Image stitching aims at stitching the images taken from different viewpoints
into an image with a wider field of view. Existing methods warp the target
image to the reference image using the estimated warp function, and a
homography is one of the most commonly used warping functions. However, when
images have large parallax due to non-planar scenes and translational motion of
a camera, the homography cannot fully describe the mapping between two images.
Existing approaches based on global or local homography estimation are not free
from this problem and suffer from undesired artifacts due to parallax. In this
paper, instead of relying on the homography-based warp, we propose a novel deep
image stitching framework exploiting the pixel-wise warp field to handle the
large-parallax problem. The proposed deep image stitching framework consists of
two modules: Pixel-wise Warping Module (PWM) and Stitched Image Generating
Module (SIGMo). PWM employs an optical flow estimation model to obtain
pixel-wise warp of the whole image, and relocates the pixels of the target
image with the obtained warp field. SIGMo blends the warped target image and
the reference image while eliminating unwanted artifacts such as misalignments,
seams, and holes that harm the plausibility of the stitched result. For
training and evaluating the proposed framework, we build a large-scale dataset
that includes image pairs with corresponding pixel-wise ground truth warp and
sample stitched result images. We show that the results of the proposed
framework are qualitatively superior to those of the conventional methods,
especially when the images have large parallax. The code and the proposed
dataset will be publicly available soon.

Computer Vision and Pattern Recognition