{"url":"/dataset/polarized-film-removal-dataset","name":"Polarized Film Removal Dataset","full_name":null,"description_markdown":"The current industrial pipeline includes 315 dynamic industrial scenarios, which can be categorized into three types: QR codes, text, and products. To enhance the diversity, we have different films with diverse material properties, coverage areas, film thicknesses, and levels of wrinkling. The film exhibits significant variability across each scenario. On the other hand, to ensure the stability of the industrial imaging pipeline, we maintained a consistent intensity level for the industrial light source and fixed the distance between the camera and the object flow. This helps to minimize the influence of errors external to the industrial system.","description_withheld":null,"homepage":"https://jqt.me/_FilmRemoval_/","introduced_date":"2024-04-30","introduced_date_note":null,"introduced_by":{"paper":"/paper/learning-to-remove-wrinkled-transparent-film","title":"Learning to Remove Wrinkled Transparent Film with Polarized Prior","first_author":"Jiaqi Tang","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Film Removal","url":"/task/film-removal","datasets_with_task":"/datasets/task/film-removal"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Polarized Film Removal Dataset"],"data_loaders":[],"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."}