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Image stitching is one of the important tasks of computer vision, which is used in many fields such as autonomous navigation and autonomous driving. However, traditional stitching methods rely too much on the quality of feature detection and show poor performance for images with few features or low resolution. Although existing deep learning-based methods can make up for the shortcomings of traditional methods, they are only used on mobile robots with smallbaseline or fixed perspectives. To address the above limitations, we propose an image stitching network consisting of three modules: multistage keypoint matching module, DFAST module and multistage image reconstruction module. First, we use a multistage keypoint matching module to align the reference image and the target image, and obtain deep homography estimates between reference and target images at different scales of features. After that, the DFAST module is designed to stitch images of arbitrary views and generate stitched feature maps at different scales. Finally, the multistage reconstruction network is used to reconstruct and optimize the stitched feature maps from feature level to pixel level and fuse stitched images of different scales to generate finer texture details. Experiments results show that our method surpasses previous methods including state-of-the-art traditional and CNN-based methods.
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