{"url":"/dataset/diode","name":"DIODE","full_name":"Dense Indoor and Outdoor Depth","description_markdown":"Diode Dense Indoor/Outdoor DEpth (**DIODE**) is the first standard dataset for monocular depth estimation comprising diverse indoor and outdoor scenes acquired with the same hardware setup. The training set consists of 8574 indoor and 16884 outdoor samples from 20 scans each. The validation set contains 325 indoor and 446 outdoor samples with each set from 10 different scans. The ground truth density for the indoor training and validation splits are approximately 99.54% and 99%, respectively. The density of the outdoor sets are naturally lower with 67.19% for training and 78.33% for validation subsets. The indoor and outdoor ranges for the dataset are 50m and 300m, respectively.\r\n\r\nSource: [Bidirectional Attention Network for Monocular Depth Estimation](https://arxiv.org/abs/2009.00743)\r\nImage Source: [https://diode-dataset.org/](https://diode-dataset.org/)","description_withheld":null,"homepage":"https://diode-dataset.org/","introduced_date":"2019-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/diode-a-dense-indoor-and-outdoor-depth","title":"DIODE: A Dense Indoor and Outdoor DEpth Dataset","first_author":"Igor Vasiljevic","url":null},"license":{"name":"MIT License","url":"https://diode-dataset.org/#diode-development-Toolkit"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"RGB-D","url":"/datasets/modality/rgb-d"}],"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":"Indoor Monocular Depth Estimation","url":"/task/indoor-monocular-depth-estimation","datasets_with_task":"/datasets/task/indoor-monocular-depth-estimation"}],"languages":[],"variants":["DIODE"],"data_loaders":[],"num_papers_in_archive":86,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/depth-estimation-on-diode","task":"Depth Estimation","dataset_variant":"DIODE","rows":2,"metrics":["Delta < 1.25","Delta < 1.25^2","Delta < 1.25^3"],"first_row_in_archive_order":{"model":"AIP-Brown","paper":"/paper/ai-playground-unreal-engine-based-data","metrics":{"Delta < 1.25":"0.3563","Delta < 1.25^2":"0.5948","Delta < 1.25^3":"0.7945"},"code_links":[{"title":"MMehdiMousavi/AIP","url":"https://github.com/MMehdiMousavi/AIP"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/indoor-monocular-depth-estimation-on-diode","task":"Indoor Monocular Depth Estimation","dataset_variant":"DIODE","rows":2,"metrics":["Delta < 1.25^3"],"first_row_in_archive_order":{"model":"LeReS","paper":"/paper/learning-to-recover-3d-scene-shape-from-a","metrics":{"Delta < 1.25^3":"0.900"},"code_links":[{"title":"aim-uofa/AdelaiDepth","url":"https://github.com/aim-uofa/AdelaiDepth"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/learning-to-recover-3d-scene-shape-from-a","title":"Learning to Recover 3D Scene Shape from a Single Image","date":"2020-12-17","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/ai-playground-unreal-engine-based-data","title":"AI Playground: Unreal Engine-based Data Ablation Tool for Deep Learning","date":"2020-07-13","rows_on_this_dataset":2,"code_links":1,"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."}