{"url":"/dataset/blurrf-synth","name":"BlurRF-Synth","full_name":null,"description_markdown":"The first large-scale dataset for training and evaluating novel-view synthesis from blurred images.\r\n\r\nThe dataset comprises a train set and a test set for each type of blur: camera motion blur and defocus blur. The train set includes 65 scenes, each containing 29 pairs of synthetically blurred images and their corresponding sharp images, captured from different viewpoints.\r\nThe test set includes 10 scenes, each with 29 blurred-sharp image pairs and five additional sharp images from different viewpoints for evaluating novel-view synthesis quality.","description_withheld":null,"homepage":"https://haeyun-choi.github.io/DDRF_page/","introduced_date":"2025-02-20","introduced_date_note":null,"introduced_by":{"paper":"/paper/exploiting-deblurring-networks-for-radiance","title":"Exploiting Deblurring Networks for Radiance Fields","first_author":"Haeyun Choi","url":null},"license":{"name":"MIT","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[{"name":"Novel View Synthesis","url":"/task/novel-view-synthesis","datasets_with_task":"/datasets/task/novel-view-synthesis"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["BlurRF-Synth"],"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."}