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.
翻译:最近,人们越来越关注一个端到端深深的基于学习的缝纫模式,然而,深深深的基于学习的缝纫最具有挑战性的一点是获取一对带有狭窄视野的输入图像和从现实世界的场景中捕捉到宽广视野的地面真实图像。为了克服这一困难,我们开发了一个薄弱的监管学习机制来培训缝纫模型,而不需要真正的地面真实图像。此外,我们提出了一个将多个真实世界的鱼眼图像作为投入的缝纫模型,并以等角投影格式制作出360个输出图像。特别是,我们的模型包括颜色一致性校正、扭曲和混合,并经过感知性和SSIM损失的培训。提议的算法的有效性在两个真实世界的缝纫数据集上得到验证。