{"url":"/dataset/uasol","name":"UASOL","full_name":"A large-scale high-resolution outdoor stereo dataset","description_markdown":"The UASOL an RGB-D stereo dataset, that contains 160902 frames, filmed at 33 different scenes, each with between 2 k and 10 k frames. The frames show different paths from the perspective of a pedestrian, including sidewalks, trails, roads, etc. The images were extracted from video files with 15 fps at HD2K resolution with a size of 2280 × 1282 pixels. The dataset also provides a GPS geolocalization tag for each second of the sequences and reflects different climatological conditions. It also involved up to 4 different persons filming the dataset at different moments of the day.\r\n\r\nWe propose a [train, validation and test split](https://www.nature.com/articles/s41597-019-0168-5/tables/4) to train the network. \r\nAdditionally, we introduce a subset of [676 pairs of RGB Stereo images and their respective depth](https://osf.io/64532/files/), which we extracted randomly from the entire dataset. This given test set is introduced to make comparability possible between the different methods trained with the dataset.","description_withheld":null,"homepage":"https://osf.io/64532/","introduced_date":"2019-08-29","introduced_date_note":null,"introduced_by":{"paper":"/paper/uasol-a-large-scale-high-resolution-outdoor","title":"UASOL, a large-scale high-resolution outdoor stereo dataset","first_author":"Zuria Bauer","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"RGB-D","url":"/datasets/modality/rgb-d"},{"name":"Stereo","url":"/datasets/modality/stereo"}],"tasks":[{"name":"Depth Estimation","url":"/task/depth-estimation","datasets_with_task":"/datasets/task/depth-estimation"},{"name":"Monocular Depth Estimation","url":"/task/monocular-depth-estimation","datasets_with_task":"/datasets/task/monocular-depth-estimation"},{"name":"Novel View Synthesis","url":"/task/novel-view-synthesis","datasets_with_task":"/datasets/task/novel-view-synthesis"},{"name":"Stereo Matching","url":"/task/stereo-matching-1","datasets_with_task":"/datasets/task/stereo-matching-1"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["UASOL"],"data_loaders":[{"repo":"https://github.com/Max-Hermann/SelfSupervisedAerialDepthEstimator","url":"https://github.com/Max-Hermann/SelfSupervisedAerialDepthEstimator","frameworks":[]}],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/monocular-depth-estimation-on-uasol","task":"Monocular Depth Estimation","dataset_variant":"UASOL","rows":1,"metrics":["RMSE"],"first_row_in_archive_order":{"model":"FCRN-DepthPrediction from Iro Laina et al. (2016)","paper":"/paper/uasol-a-large-scale-high-resolution-outdoor","metrics":{"RMSE":"8.119"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/uasol-a-large-scale-high-resolution-outdoor","title":"UASOL, a large-scale high-resolution outdoor stereo dataset","date":"2019-08-29","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"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."}