{"url":"/dataset/oxford-robotcar-dataset","name":"Oxford RobotCar Dataset","full_name":null,"description_markdown":"The Oxford RobotCar Dataset contains over 100 repetitions of a consistent route through Oxford, UK, captured over a period of over a year. The dataset captures many different combinations of weather, traffic and pedestrians, along with longer term changes such as construction and roadworks.\r\n\r\nSource: [Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset](/paper/real-time-kinematic-ground-truth-for-the)","description_withheld":null,"homepage":"http://robotcar-dataset.robots.ox.ac.uk/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/real-time-kinematic-ground-truth-for-the","title":"Real-time Kinematic Ground Truth for the Oxford RobotCar Dataset","first_author":"Will Maddern","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Unsupervised Domain Adaptation","url":"/task/unsupervised-domain-adaptation","datasets_with_task":"/datasets/task/unsupervised-domain-adaptation"},{"name":"Visual Place Recognition","url":"/task/visual-place-recognition","datasets_with_task":"/datasets/task/visual-place-recognition"},{"name":"3D Place Recognition","url":"/task/3d-place-recognition","datasets_with_task":"/datasets/task/3d-place-recognition"}],"languages":[],"variants":["Oxford RobotCar Dataset","Cityscapes-to-OxfordCar"],"data_loaders":[],"num_papers_in_archive":42,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-place-recognition-on-oxford-robotcar","task":"3D Place Recognition","dataset_variant":"Oxford RobotCar Dataset","rows":10,"metrics":["AR@1","AR@1%"],"first_row_in_archive_order":{"model":"CrossLoc3D","paper":"/paper/crossloc3d-aerial-ground-cross-source-3d","metrics":{"AR@1":"94.36","AR@1%":"98.59"},"code_links":[{"title":"rayguan97/crossloc3d","url":"https://github.com/rayguan97/crossloc3d"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/visual-place-recognition-on-oxford-robotcar-4","task":"Visual Place Recognition","dataset_variant":"Oxford RobotCar Dataset","rows":7,"metrics":["Recall@1"],"first_row_in_archive_order":{"model":"AnyLoc-VLAD-DINOv2","paper":"/paper/anyloc-towards-universal-visual-place","metrics":{"Recall@1":"98.95"},"code_links":[{"title":"AnyLoc/AnyLoc","url":"https://github.com/AnyLoc/AnyLoc"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-cityscapes-2","task":"Unsupervised Domain Adaptation","dataset_variant":"Cityscapes-to-OxfordCar","rows":4,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"Uncertainty + Adaboost","paper":"/paper/adaptive-boosting-for-domain-adaptation","metrics":{"mIoU":"75.2"},"code_links":[{"title":"layumi/AdaBoost_Seg","url":"https://github.com/layumi/AdaBoost_Seg"},{"title":"layumi/Cifar10-Adaboost","url":"https://github.com/layumi/Cifar10-Adaboost"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/anyloc-towards-universal-visual-place","title":"AnyLoc: Towards Universal Visual Place Recognition","date":"2023-08-01","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dinov2-learning-robust-visual-features","title":"DINOv2: Learning Robust Visual Features without Supervision","date":"2023-04-14","rows_on_this_dataset":1,"code_links":26,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":46,"samples_ran":21,"samples_unverified":25,"pointer_only_for_licence":12,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/crossloc3d-aerial-ground-cross-source-3d","title":"CrossLoc3D: Aerial-Ground Cross-Source 3D Place Recognition","date":"2023-03-31","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":11,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mixvpr-feature-mixing-for-visual-place","title":"MixVPR: Feature Mixing for Visual Place Recognition","date":"2023-03-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rethinking-visual-geo-localization-for-large","title":"Rethinking Visual Geo-localization for Large-Scale Applications","date":"2022-04-05","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":6,"samples_unverified":4,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/minkloc3d-si-3d-lidar-place-recognition-with","title":"MinkLoc3D-SI: 3D LiDAR place recognition with sparse convolutions, spherical coordinates, and intensity","date":"2021-12-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/svt-net-a-super-light-weight-network-for","title":"SVT-Net: Super Light-Weight Sparse Voxel Transformer for Large Scale Place Recognition","date":"2021-05-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/emerging-properties-in-self-supervised-vision","title":"Emerging Properties in Self-Supervised Vision Transformers","date":"2021-04-29","rows_on_this_dataset":1,"code_links":32,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":5,"samples_unverified":15,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/adaptive-boosting-for-domain-adaptation","title":"Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation","date":"2021-03-29","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/pyramid-point-cloud-transformer-for-large","title":"Pyramid Point Cloud Transformer for Large-Scale Place Recognition","date":"2021-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/soe-net-a-self-attention-and-orientation","title":"SOE-Net: A Self-Attention and Orientation Encoding Network for Point Cloud based Place Recognition","date":"2020-11-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dh3d-deep-hierarchical-3d-descriptors-for","title":"DH3D: Deep Hierarchical 3D Descriptors for Robust Large-Scale 6DoF Relocalization","date":"2020-07-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rectifying-pseudo-label-learning-via","title":"Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation","date":"2020-03-08","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unsupervised-scene-adaptation-with-memory","title":"Unsupervised Scene Adaptation with Memory Regularization in vivo","date":"2019-12-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pcan-3d-attention-map-learning-using","title":"PCAN: 3D Attention Map Learning Using Contextual Information for Point Cloud Based Retrieval","date":"2019-04-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/181207050","title":"LPD-Net: 3D Point Cloud Learning for Large-Scale Place Recognition and Environment Analysis","date":"2018-12-11","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/pointnetvlad-deep-point-cloud-based-retrieval","title":"PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition","date":"2018-04-10","rows_on_this_dataset":1,"code_links":6,"syntology":null},{"paper":"/paper/netvlad-cnn-architecture-for-weakly","title":"NetVLAD: CNN architecture for weakly supervised place recognition","date":"2015-11-23","rows_on_this_dataset":1,"code_links":14,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":0,"samples_unverified":12,"pointer_only_for_licence":0,"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":8,"samples_harvested":110,"samples_ran":50,"samples_unverified":60,"pointer_only_for_licence":20,"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."}