{"url":"/dataset/pds-coco","name":"PDS-COCO","full_name":"Photometrically Distorted Synthetic COCO","description_markdown":"Photometrically Distorted Synthetic COCO (PDS-COCO) dataset is a synthetically created dataset for homography estimation learning. The idea is exactly the same as in the Synthetic [COCO (S-COCO)](https://paperswithcode.com/dataset/s-coco) dataset with SSD-like image distortion added at the beginning of the whole procedure: the first step involves adjusting the brightness of the image using randomly picked value $\\delta_b \\in \\mathcal{U}(-32, 32)$. Next, contrast, saturation and hue noise is applied with the following values: $\\delta_c \\in \\mathcal{U}(0.5, 1.5)$, $\\delta_s \\in \\mathcal{U}(0.5, 1.5)$ and $\\delta_h \\in \\mathcal{U}(-18, 18)$. Finally, the color channels of the image are randomly swapped with a probability of $0.5$. Such a photometric distortion procedure is applied to the original image independently to create source and target candidates.","description_withheld":null,"homepage":"","introduced_date":"2021-04-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/perceptual-loss-for-robust-unsupervised","title":"Perceptual Loss for Robust Unsupervised Homography Estimation","first_author":"Daniel Koguciuk","url":null},"license":{"name":"Custom","url":"https://cocodataset.org/#termsofuse"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Homography Estimation","url":"/task/homography-estimation","datasets_with_task":"/datasets/task/homography-estimation"}],"languages":[],"variants":["PDS-COCO"],"data_loaders":[{"repo":"https://github.com/NeurAI-Lab/biHomE","url":"https://github.com/NeurAI-Lab/biHomE","frameworks":["pytorch"]}],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/homography-estimation-on-pds-coco","task":"Homography Estimation","dataset_variant":"PDS-COCO","rows":3,"metrics":["MACE"],"first_row_in_archive_order":{"model":"PFNet+biHomE","paper":"/paper/perceptual-loss-for-robust-unsupervised","metrics":{"MACE":"2.11"},"code_links":[{"title":"NeurAI-Lab/biHomE","url":"https://github.com/NeurAI-Lab/biHomE"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/perceptual-loss-for-robust-unsupervised","title":"Perceptual Loss for Robust Unsupervised Homography Estimation","date":"2021-04-20","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/rethinking-planar-homography-estimation-using","title":"Rethinking Planar Homography Estimation Using Perspective Fields","date":"2019-05-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-image-homography-estimation","title":"Deep Image Homography Estimation","date":"2016-06-13","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":1,"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."}