AutoPanoStitch image stitching datasets compilation: A compilation of image stitching datasets from USC during my Ph.D. days and other open source providers.

Creating image stitching datasets takes a lot of time and effort. During my Ph.D. days, I tried to compile datasets that were comprehensive to have spherical, cylindrical or planar and full view 360 x 180-degree panoramas. These datasets posed a real challenge to the automatic stitching method. If all these datasets are stitched well, it definitely shows the robustness of your stitching method.

All these datasets are public! Some of them were from my Ph.D. studies (especially on cracks) and most of them were downloaded from the internet. I do not remember the individual names of the dataset providers. But I acknowledge their work and I am thankful to all of them! I hope you appreciate their efforts in making these datasets public to advance the research!

Below are some samples from the datasets. There are 85 panorama or image stitching/registration datasets in total. You can download them in AutoPanoStitch Stitching Datasets Compilation. If I come across any interesting and challenging datasets, I will expand this compilation.

Type	Images
CMU
Grand Canyon
Shanghai
UCSB
Yellowstone
Rio

Image stitching datasets for cracks are available to the public. If you use this specific dataset (related to cracks) in your research, please use the following BibTeX entry to cite:

@PhdThesis{preetham2021vision,
author = {{Aghalaya Manjunatha}, Preetham},
title = {Vision-Based and Data-Driven Analytical and Experimental Studies into Condition Assessment and Change Detection of Evolving Civil, Mechanical and Aerospace Infrastructures},
school =  {University of Southern California},
year = 2021,
type = {Dissertations & Theses},
address = {3550 Trousdale Parkway Los Angeles, CA 90089},
month = {December},
note = {Condition assessment, Crack localization, Crack change detection, Synthetic crack generation, Sewer pipe condition assessment, Mechanical systems defect detection and quantification}
}

I am thankful to all the authors who made the image stitching datasets public.

Please rate and provide feedback for further improvements.