论文标题
现实世界全景图的弱监督缝线网络
Weakly-Supervised Stitching Network for Real-World Panoramic Image Generation
论文作者
论文摘要
最近，人们对端到端的基于深度学习的缝线模型的关注越来越大。但是，基于深度学习的缝线中最具挑战性的点是获得成对的输入图像，这些图像具有狭窄的视野和地面真相图像，并具有从现实世界场景中捕获的广阔视野。为了克服这一困难，我们开发了一种弱监督的学习机制来训练缝线模型而无需真正的地面真相图像。此外，我们提出了一个缝合模型，该模型将多个现实世界的鱼眼图像作为输入，并以等应角投影格式创建360个输出图像。特别是，我们的模型由颜色一致性校正，扭曲和混合组成，并受到感知和SSIM损失的训练。在两个实际缝合数据集上验证了所提出的算法的有效性。
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.
