{"url":"/dataset/aachen-day-night-v1-1-benchmark","name":"Aachen Day-Night v1.1 Benchmark","full_name":null,"description_markdown":"Aachen Day-Night v1.1 dataset is an extended version of the original *[Aachen Day-Night dataset](https://paperswithcode.com/dataset/aachen-day-night)*. Besides the original query images, the Aachen Day-Night v1.1 dataset contains an additional 93 nighttime queries. In addition, it uses a larger 3D model containing additional images. These additional images were extracted from video sequences captured with different \r\ncameras. Please refer to *[Reference Pose Generation for Long-term Visual Localization via Learned Features and View Synthesis](https://arxiv.org/abs/2005.05179)* for more information.","description_withheld":null,"homepage":"https://www.visuallocalization.net/datasets/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Visual Localization","url":"/task/visual-localization","datasets_with_task":"/datasets/task/visual-localization"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["Aachen Day-Night v1.1 Benchmark"],"data_loaders":[],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/visual-localization-on-aachen-day-night-v1-1","task":"Visual Localization","dataset_variant":"Aachen Day-Night v1.1 Benchmark","rows":7,"metrics":["Acc@0.25m, 2°","Acc@0.5m, 5°","Acc@5m, 10°"],"first_row_in_archive_order":{"model":"GIM-LoFTR","paper":"/paper/gim-learning-generalizable-image-matcher-from","metrics":{"Acc@0.25m, 2°":"79.1","Acc@0.5m, 5°":"91.6","Acc@5m, 10°":"100.0"},"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/shared-coupling-bridge-for-weakly-supervised","title":"Shared Coupling-bridge for Weakly Supervised Local Feature Learning","date":"2022-12-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"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."}