{"url":"/dataset/cross","name":"CROSS","full_name":"Cross-Reference Omnidirectional Stitching IQA","description_markdown":"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.\r\n\r\nSource: [Image Quality Assessment for Omnidirectional Cross-reference Stitching](https://arxiv.org/pdf/1904.04960.pdf)","description_withheld":null,"homepage":"http://cvteam.net/","introduced_date":"2019-04-10","introduced_date_note":null,"introduced_by":{"paper":"/paper/image-quality-assessment-for-omnidirectional","title":"Image Quality Assessment for Omnidirectional Cross-reference Stitching","first_author":"Kaiwen Yu","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Quality Assessment","url":"/task/image-quality-assessment","datasets_with_task":"/datasets/task/image-quality-assessment"},{"name":"Image Stitching","url":"/task/image-stitching","datasets_with_task":"/datasets/task/image-stitching"}],"languages":[],"variants":["CROSS"],"data_loaders":[{"repo":"https://github.com/denght19/fisheeye","url":"https://github.com/denght19/fisheeye","frameworks":["pytorch"]}],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}