Image Stitching is a process of composing multiple images with narrow but overlapping fields of view to create a larger image with a wider field of view.
UDIS-D is a large image dataset for image stitching or image registration. It contains different overlap rates, varying degrees of parallax, and variable scenes such as indoor, outdoor, night, dark, snow, and zooming.
The PhotoSynth (PS) dataset for patch matching consists of a total of 30 scenes with 25 scenes for training and 5 scenes for validation. The different image pairs are captured in different illumination conditions, at different scales and with different viewpoints. Source: https://arxiv.org/abs/1801.01466 Image Source: https://github.com/rmitra/PS-Dataset
Cross-Reference Omnidirectional Stitching IQA is a novel omnidirectional image dataset containing stitched images as well as dual-fisheye images captured from standard quarters of 0◦, 90◦ , 180◦ and 270◦. In this manner, when evaluating the quality of an image stitched from a pair of fisheye images (e.g., 0◦ and 180◦), the other pair of fisheye images (e.g., 90◦ and 270◦) can be used as the cross-reference to provide ground-truth observations of the stitching regions. Source: Image Quality Assessment for Omnidirectional Cross-reference Stitching