{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/uasol-a-large-scale-high-resolution-outdoor","title":"UASOL, a large-scale high-resolution outdoor stereo dataset","arxiv_id":null,"date":"2019-08-29","proceeding":null,"authors":["Zuria Bauer","Francisco Gomez-Donoso","Edmanuel Cruz","Sergio Orts-Escolano","Miguel Cazorla"],"abstract":"In this paper, we propose a new dataset for outdoor depth estimation from single and stereo RGB images. The dataset was acquired from the point of view of a pedestrian. Currently, the most novel approaches take advantage of deep learning-based techniques, which have proven to outperform traditional state-of-the-art computer vision methods. Nonetheless, these methods require large amounts of reliable ground-truth data. Despite their already existing several datasets that could be used for depth estimation, almost none of them are outdoor-oriented from an egocentric point of view. Our dataset introduces a large number of high-definition pairs of color frames and corresponding depth maps from a human perspective. In addition, the proposed dataset also features human interaction and great variability of data, as shown in this work.","url_abs":"https://www.nature.com/articles/s41597-019-0168-5","url_pdf":"https://www.nature.com/articles/s41597-019-0168-5.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"monocular-depth-estimation","task_name":"Monocular Depth Estimation"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[],"datasets_introduced":[{"slug":"uasol","name":"UASOL","full_name":"A large-scale high-resolution outdoor stereo dataset"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/monocular-depth-estimation-on-uasol","task":"Monocular Depth Estimation","dataset":"UASOL","model":"FCRN-DepthPrediction from Iro Laina et al. (2016)","rank_in_archive_order":1,"of":1,"metrics":{"RMSE":"8.119"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}