{"url":"/dataset/learning-to-autofocus","name":"Learning to Autofocus","full_name":null,"description_markdown":"This dataset contains 510 focal stacks (49 different focal distances) from in-the-wild scenes with calculated depth from SFM. This dataset was designed for research on Autofocus but can be used for any research which is interested in focal stacks, defocus cues, or depth signals (particularly for interest in close depth).","description_withheld":null,"homepage":"https://learntoautofocus-google.github.io/","introduced_date":"2020-04-26","introduced_date_note":null,"introduced_by":{"paper":"/paper/learning-to-autofocus","title":"Learning to Autofocus","first_author":"Charles Herrmann","url":null},"license":{"name":"MIT","url":null},"modalities":[],"tasks":[],"languages":[],"variants":["Learning to Autofocus"],"data_loaders":[],"num_papers_in_archive":2,"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."}