{"url":"/dataset/middlebury-2014","name":"Middlebury 2014","full_name":"Middlebury 2014","description_markdown":"The **Middlebury 2014** dataset contains a set of 23 high resolution stereo pairs for which known camera calibration parameters and ground truth disparity maps obtained with a structured light scanner are available. The images in the Middlebury dataset all show static indoor scenes with varying difficulties including repetitive structures, occlusions, wiry objects as well as untextured areas.\r\n\r\nSource: [Using Self-Contradiction to Learn Confidence Measures in Stereo Vision](https://arxiv.org/abs/1604.05132)\r\nImage Source: [https://vision.middlebury.edu/stereo/data/scenes2014/](https://vision.middlebury.edu/stereo/data/scenes2014/)","description_withheld":null,"homepage":"https://vision.middlebury.edu/stereo/data/scenes2014/","introduced_date":"2014-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth","first_author":null,"url":"https://doi.org/10.1007/978-3-319-11752-2_3"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"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":"Disparity Estimation","url":"/task/disparity-estimation","datasets_with_task":"/datasets/task/disparity-estimation"},{"name":"Stereo Disparity Estimation","url":"/task/stereo-disparity-estimation","datasets_with_task":"/datasets/task/stereo-disparity-estimation"},{"name":"Stereo Matching","url":"/task/stereo-matching-1","datasets_with_task":"/datasets/task/stereo-matching-1"}],"languages":[],"variants":["Middlebury 2014"],"data_loaders":[],"num_papers_in_archive":59,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/monocular-depth-estimation-on-middlebury-2014","task":"Monocular Depth Estimation","dataset_variant":"Middlebury 2014","rows":2,"metrics":["ORD ","D3R","RMSE","δ1.25"],"first_row_in_archive_order":{"model":"Miangoleh et al. (MiDaS)","paper":"/paper/boosting-monocular-depth-estimation-models-to","metrics":{"D3R":"0.1578","ORD ":"0.3467","RMSE":"0.1557","δ1.25":"0.7406"},"code_links":[{"title":"compphoto/BoostingMonocularDepth","url":"https://github.com/compphoto/BoostingMonocularDepth"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/stereo-disparity-estimation-on-middlebury","task":"Stereo Disparity Estimation","dataset_variant":"Middlebury 2014","rows":2,"metrics":["D1 Error (2px)"],"first_row_in_archive_order":{"model":"MoCha-V2","paper":"/paper/mocha-stereo-motif-channel-attention-network","metrics":{"D1 Error (2px)":"3.51"},"code_links":[{"title":"zyangchen/mocha-stereo","url":"https://github.com/zyangchen/mocha-stereo"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/mocha-stereo-motif-channel-attention-network","title":"MoCha-Stereo: Motif Channel Attention Network for Stereo Matching","date":"2024-04-10","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":16,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/raft-stereo-multilevel-recurrent-field","title":"RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching","date":"2021-09-15","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/boosting-monocular-depth-estimation-models-to","title":"Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution Merging","date":"2021-05-28","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"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":3,"samples_harvested":21,"samples_ran":19,"samples_unverified":2,"pointer_only_for_licence":3,"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."}