{"url":"/dataset/hlw","name":"HLW","full_name":"Horizon Lines in the Wild","description_markdown":"We introduce Horizon Lines in the Wild (HLW), a large dataset of real-world images with\r\nlabeled horizon lines, captured in a diverse set of environments. The dataset is available\r\nfor download at our project website [1]. We begin by characterizing limitations in existing\r\ndatasets for evaluating horizon line detection methods and then describe our approach for\r\nleveraging structure from motion to automatically label images with horizon lines.","description_withheld":null,"homepage":"https://mvrl.cse.wustl.edu/datasets/hlw/","introduced_date":"2016-04-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/horizon-lines-in-the-wild","title":"Horizon Lines in the Wild","first_author":"Scott Workman","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["HLW"],"data_loaders":[],"num_papers_in_archive":13,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/horizon-line-estimation-on-horizon-lines-in","task":"Horizon Line Estimation","dataset_variant":"Horizon Lines in the Wild","rows":5,"metrics":["AUC (horizon error)"],"first_row_in_archive_order":{"model":"NG-DSAC","paper":"/paper/neural-guided-ransac-learning-where-to-sample","metrics":{"AUC (horizon error)":"75.2"},"code_links":[{"title":"vislearn/ngransac","url":"https://github.com/vislearn/ngransac"},{"title":"vislearn/ngdsac_camreloc","url":"https://github.com/vislearn/ngdsac_camreloc"},{"title":"vislearn/ngdsac_horizon","url":"https://github.com/vislearn/ngdsac_horizon"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/neural-guided-ransac-learning-where-to-sample","title":"Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses","date":"2019-05-10","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":4,"samples_ran":0,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/a-contrario-horizon-first-vanishing-point","title":"A-Contrario Horizon-First Vanishing Point Detection Using Second-Order Grouping Laws","date":"2018-09-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-learning-for-vanishing-point-detection","title":"Deep Learning for Vanishing Point Detection Using an Inverse Gnomonic Projection","date":"2017-07-08","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/detecting-vanishing-points-using-global-image","title":"Detecting Vanishing Points using Global Image Context in a Non-Manhattan World","date":"2016-08-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/horizon-lines-in-the-wild","title":"Horizon Lines in the Wild","date":"2016-04-07","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":1,"samples_harvested":4,"samples_ran":0,"samples_unverified":4,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"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."}