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.

提出了一种基于深度学习的缝合模型，采用弱监督学习机制训练，实现对多个鱼眼图像的 360 度输出，通过色彩一致性校正、畸变校正和图像融合实现，其训练采用感知损失和 SSIM 损失函数，在两个真实的缝合数据集上进行了有效性验证。