  Recently, there has been growing attention on an end-to-end deep
learning-based stitching model. However, the most challenging point in deep
learning-based stitching is to obtain pairs of input images with a narrow field
of view and ground truth images with a wide field of view captured from
real-world scenes. To overcome this difficulty, we develop a weakly-supervised
learning mechanism to train the stitching model without requiring genuine
ground truth images. In addition, we propose a stitching model that takes
multiple real-world fisheye images as inputs and creates a 360 output image in
an equirectangular projection format. In particular, our model consists of
color consistency corrections, warping, and blending, and is trained by
perceptual and SSIM losses. The effectiveness of the proposed algorithm is
verified on two real-world stitching datasets.
