In this letter, we propose an end-to-end stitching network, which takes two images with a narrow field of view (FOV) as inputs, and produces a single image with a wide FOV. Our method estimates multiple homographies to cover the depth differences in the scene and is therefore robust against parallax distortion. In particular, global warping maps are generated using estimated multiple homographies and adjusted by local displacement maps. The final result is made by warping input images multiple times using the warping maps and then merging warped images with the weight maps. Multiple homographies, local displacement maps, and weight maps are generated simultaneously by our stitching network. To train the stitching network, we construct a dataset using the CARLA simulator. Then, using this dataset, our network is trained by end-to-end supervised learning based on appearance matching loss and depth layer loss. In experiments, we show that our method is superior to existing methods both qualitatively and quantitatively. Also, we provide various empirical studies for in-depth analysis as well as the result of the expansion to 360 $^{\circ }$ panoramas.

中文翻译：

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通过多象素估计的端到端图像拼接网络

在这封信中，我们提出了一个端到端的拼接网络，该网络将两个具有窄视场（FOV）的图像作为输入，并生成一个具有宽FOV的图像。我们的方法估计了多个单应性以覆盖场景中的深度差异，因此对视差失真具有鲁棒性。特别是，使用估计的多个单应性生成全局变形图，并通过局部位移图进行调整。通过使用变形图对输入图像进行多次变形，然后将变形后的图像与权重图合并，可以得到最终结果。我们的拼接网络可同时生成多个单应性图，局部位移图和权重图。为了训练缝合网络，我们使用CARLA模拟器构建了一个数据集。然后，使用此数据集，我们的网络是通过基于外观匹配损失和深度层损失的端到端监督学习进行训练的。在实验中，我们证明了我们的方法在质量和数量上均优于现有方法。此外，我们提供各种经验研究以进行深入分析，以及扩展到360度的结果 $ ^ {\ circ} $ 全景图。