{"url":"/dataset/depth-vidit","name":"Depth VIDIT","full_name":"Virtual Image Dataset for Illumination Transfer","description_markdown":"VIDIT is a reference evaluation benchmark and to push forward the development of illumination manipulation methods. Virtual datasets are not only an important step towards achieving real-image performance but have also proven capable of improving training even when real datasets are possible to acquire and available. VIDIT contains 300 virtual scenes used for training, where every scene is captured 40 times in total: from 8 equally-spaced azimuthal angles, each lit with 5 different illuminants.","description_withheld":null,"homepage":"https://github.com/majedelhelou/VIDIT","introduced_date":"2021-06-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/ntire-2021-depth-guided-image-relighting","title":"NTIRE 2021 Depth Guided Image Relighting Challenge","first_author":"Majed El Helou","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["Depth VIDIT"],"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":"2026-09-25T09:33:49+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."}