{"url":"/dataset/inloc","name":"InLoc","full_name":null,"description_markdown":"InLoc is a dataset with reference 6DoF poses for large-scale indoor localization. Query photographs are captured by mobile phones at a different time than the reference 3D map, thus presenting a realistic indoor localization scenario.\r\n\r\nSource: [InLoc: Indoor Visual Localization with Dense Matching and View Synthesis](/paper/inloc-indoor-visual-localization-with-dense)","description_withheld":null,"homepage":"http://www.ok.sc.e.titech.ac.jp/INLOC/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/inloc-indoor-visual-localization-with-dense","title":"InLoc: Indoor Visual Localization with Dense Matching and View Synthesis","first_author":"Hajime Taira","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Pose Estimation","url":"/task/pose-estimation","datasets_with_task":"/datasets/task/pose-estimation"},{"name":"Image Retrieval","url":"/task/image-retrieval","datasets_with_task":"/datasets/task/image-retrieval"},{"name":"Visual Localization","url":"/task/visual-localization","datasets_with_task":"/datasets/task/visual-localization"}],"languages":[],"variants":["InLoc"],"data_loaders":[],"num_papers_in_archive":67,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/pose-estimation-on-inloc","task":"Pose Estimation","dataset_variant":"InLoc","rows":6,"metrics":["DUC1-Acc@0.25m,10°","DUC1-Acc@0.5m,10°","DUC1-Acc@1.0m,10°","DUC2-Acc@0.25m,10°","DUC2-Acc@0.5m,10°","DUC2-Acc@1.0m,10°"],"first_row_in_archive_order":{"model":"GIM-DKM","paper":"/paper/gim-learning-generalizable-image-matcher-from","metrics":{"DUC1-Acc@0.25m,10°":"57.1","DUC1-Acc@0.5m,10°":"78.8","DUC1-Acc@1.0m,10°":"88.4","DUC2-Acc@0.25m,10°":"70.2","DUC2-Acc@0.5m,10°":"91.6","DUC2-Acc@1.0m,10°":"92.4"},"code_links":[{"title":"xuelunshen/gim","url":"https://github.com/xuelunshen/gim"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/gim-learning-generalizable-image-matcher-from","title":"GIM: Learning Generalizable Image Matcher From Internet Videos","date":"2024-02-16","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":12,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-kernelized-dense-geometric-matching","title":"DKM: Dense Kernelized Feature Matching for Geometry Estimation","date":"2022-02-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/loftr-detector-free-local-feature-matching","title":"LoFTR: Detector-Free Local Feature Matching with Transformers","date":"2021-04-01","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":16,"samples_ran":14,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/superglue-learning-feature-matching-with","title":"SuperGlue: Learning Feature Matching with Graph Neural Networks","date":"2019-11-26","rows_on_this_dataset":1,"code_links":19,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":22,"samples_ran":6,"samples_unverified":16,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":55,"samples_ran":32,"samples_unverified":23,"pointer_only_for_licence":6,"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."}