{"url":"/dataset/ddad","name":"DDAD","full_name":"Dense Depth for Autonomous Driving","description_markdown":"**DDAD** is a new autonomous driving benchmark from TRI (Toyota Research Institute) for long range (up to 250m) and dense depth estimation in challenging and diverse urban conditions. It contains monocular videos and accurate ground-truth depth (across a full 360 degree field of view) generated from high-density LiDARs mounted on a fleet of self-driving cars operating in a cross-continental setting. DDAD contains scenes from urban settings in the United States (San Francisco, Bay Area, Cambridge, Detroit, Ann Arbor) and Japan (Tokyo, Odaiba).\n\nSource: [https://github.com/TRI-ML/DDAD](https://github.com/TRI-ML/DDAD)\nImage Source: [https://github.com/TRI-ML/DDAD](https://github.com/TRI-ML/DDAD)","description_withheld":null,"homepage":"https://github.com/TRI-ML/DDAD","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/packnet-sfm-3d-packing-for-self-supervised","title":"3D Packing for Self-Supervised Monocular Depth Estimation","first_author":"Vitor Guizilini","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"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":"Self-Driving Cars","url":"/task/self-driving-cars","datasets_with_task":"/datasets/task/self-driving-cars"}],"languages":[],"variants":["DDAD"],"data_loaders":[{"repo":"https://github.com/TRI-ML/DDAD","url":"https://github.com/TRI-ML/DDAD","frameworks":["pytorch"]}],"num_papers_in_archive":73,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/monocular-depth-estimation-on-ddad","task":"Monocular Depth Estimation","dataset_variant":"DDAD","rows":4,"metrics":["RMSE","RMSE log","Sq Rel","absolute relative error","Delta < 1.25"],"first_row_in_archive_order":{"model":"AFNet","paper":"/paper/adaptive-fusion-of-single-view-and-multi-view","metrics":{"RMSE":"4.60","RMSE log":"0.154","Sq Rel":"0.979","absolute relative error":"0.088"},"code_links":[{"title":"junda24/afnet","url":"https://github.com/junda24/afnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/scaledepth-decomposing-metric-depth","title":"ScaleDepth: Decomposing Metric Depth Estimation into Scale Prediction and Relative Depth Estimation","date":"2024-07-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/adaptive-fusion-of-single-view-and-multi-view","title":"Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving","date":"2024-03-12","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":8,"samples_ran":7,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/gedepth-ground-embedding-for-monocular-depth","title":"GEDepth: Ground Embedding for Monocular Depth Estimation","date":"2023-09-18","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/transdssl-transformer-based-depth-estimation","title":"TransDSSL: Transformer based Depth Estimation via Self-Supervised Learning","date":"2022-08-05","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":1,"samples_harvested":8,"samples_ran":7,"samples_unverified":1,"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."}