{"url":"/dataset/middlebury","name":"Middlebury","full_name":"Middlebury Stereo","description_markdown":"The **Middlebury** Stereo dataset consists of high-resolution stereo sequences with complex geometry and pixel-accurate ground-truth disparity data. The ground-truth disparities are acquired using a novel technique that employs structured lighting and does not require the calibration of the light projectors.\r\n\r\nSource: [https://vision.middlebury.edu/stereo/data/](https://vision.middlebury.edu/stereo/data/)\r\nImage Source: [https://www.researchgate.net/figure/The-stereo-matching-results-on-the-Middlebury-dataset-From-left-to-right-each-set-of_fig3_273399625](https://www.researchgate.net/figure/The-stereo-matching-results-on-the-Middlebury-dataset-From-left-to-right-each-set-of_fig3_273399625)","description_withheld":null,"homepage":"https://vision.middlebury.edu/stereo/data/","introduced_date":"2002-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms","first_author":null,"url":"https://doi.org/10.1023/A:1014573219977"},"license":{"name":"Custom","url":"https://vision.middlebury.edu/stereo/data/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Stereo","url":"/datasets/modality/stereo"}],"tasks":[{"name":"Image Super-Resolution","url":"/task/image-super-resolution","datasets_with_task":"/datasets/task/image-super-resolution"},{"name":"Depth Estimation","url":"/task/depth-estimation","datasets_with_task":"/datasets/task/depth-estimation"},{"name":"Video Frame Interpolation","url":"/task/video-frame-interpolation","datasets_with_task":"/datasets/task/video-frame-interpolation"},{"name":"Stereo Image Super-Resolution","url":"/task/stereo-image-super-resolution","datasets_with_task":"/datasets/task/stereo-image-super-resolution"}],"languages":[],"variants":["Middlebury","Middlebury - 2x upscaling","Middlebury - 4x upscaling"],"data_loaders":[],"num_papers_in_archive":223,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/video-frame-interpolation-on-middlebury","task":"Video Frame Interpolation","dataset_variant":"Middlebury","rows":11,"metrics":["Interpolation Error","PSNR","SSIM","LPIPS"],"first_row_in_archive_order":{"model":"IFRNet","paper":"/paper/ifrnet-intermediate-feature-refine-network","metrics":{"Interpolation Error":"4.216"},"code_links":[{"title":"ltkong218/ifrnet","url":"https://github.com/ltkong218/ifrnet"},{"title":"pilot7747/sldl","url":"https://github.com/pilot7747/sldl"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/stereo-image-super-resolution-on-middlebury","task":"Stereo Image Super-Resolution","dataset_variant":"Middlebury - 4x upscaling","rows":9,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"SwinFIRSSR","paper":"/paper/swinfir-revisiting-the-swinir-with-fast","metrics":{"PSNR":"30.44"},"code_links":[{"title":"Zdafeng/SwinFIR","url":"https://github.com/Zdafeng/SwinFIR"},{"title":"IMPLabUniPr/swin2-mose","url":"https://github.com/IMPLabUniPr/swin2-mose"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/stereo-image-super-resolution-on-middlebury-1","task":"Stereo Image Super-Resolution","dataset_variant":"Middlebury - 2x upscaling","rows":9,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"ASteISR","paper":"/paper/asteisr-adapting-single-image-super","metrics":{"PSNR":"36.60"},"code_links":[{"title":"fzuzyb/ASteISR","url":"https://github.com/fzuzyb/ASteISR"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-super-resolution-on-middlebury-2x","task":"Image Super-Resolution","dataset_variant":"Middlebury - 2x upscaling","rows":1,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"PASSRnet","paper":"/paper/learning-parallax-attention-for-stereo-image","metrics":{"PSNR":"34.05"},"code_links":[{"title":"LongguangWang/PASSRnet","url":"https://github.com/LongguangWang/PASSRnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-super-resolution-on-middlebury-4x","task":"Image Super-Resolution","dataset_variant":"Middlebury - 4x upscaling","rows":1,"metrics":["PSNR"],"first_row_in_archive_order":{"model":"PASSRnet","paper":"/paper/learning-parallax-attention-for-stereo-image","metrics":{"PSNR":"28.63"},"code_links":[{"title":"LongguangWang/PASSRnet","url":"https://github.com/LongguangWang/PASSRnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/learning-optimal-combination-patterns-for","title":"Learning Optimal Combination Patterns for Lightweight Stereo Image Super-Resolution","date":"2024-10-28","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/asteisr-adapting-single-image-super","title":"ASteISR: Adapting Single Image Super-resolution Pre-trained Model for Efficient Stereo Image Super-resolution","date":"2024-07-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-accurate-and-enriched-features-for","title":"Learning Accurate and Enriched Features for Stereo Image Super-Resolution","date":"2024-06-23","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/swinfir-revisiting-the-swinir-with-fast","title":"SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution","date":"2022-08-24","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/ifrnet-intermediate-feature-refine-network","title":"IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation","date":"2022-05-29","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":10,"samples_unverified":10,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/nafssr-stereo-image-super-resolution-using","title":"NAFSSR: Stereo Image Super-Resolution Using NAFNet","date":"2022-04-19","rows_on_this_dataset":4,"code_links":5,"syntology":null},{"paper":"/paper/exploring-motion-ambiguity-and-alignment-for","title":"Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation","date":"2022-03-19","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/film-frame-interpolation-for-large-motion","title":"FILM: Frame Interpolation for Large Motion","date":"2022-02-10","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":10,"samples_unverified":10,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/feedback-network-for-mutually-boosted-stereo","title":"Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation","date":"2021-06-02","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/cdfi-compression-driven-network-design-for","title":"CDFI: Compression-Driven Network Design for Frame Interpolation","date":"2021-03-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/symmetric-parallax-attention-for-stereo-image","title":"Symmetric Parallax Attention for Stereo Image Super-Resolution","date":"2020-11-07","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/bmbc-bilateral-motion-estimation-with","title":"BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation","date":"2020-07-17","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":3,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/softmax-splatting-for-video-frame","title":"Softmax Splatting for Video Frame Interpolation","date":"2020-03-11","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/depth-aware-video-frame-interpolation","title":"Depth-Aware Video Frame Interpolation","date":"2019-04-01","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":3,"samples_unverified":12,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-parallax-attention-for-stereo-image","title":"Learning Parallax Attention for Stereo Image Super-Resolution","date":"2019-03-14","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":0,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/memc-net-motion-estimation-and-motion-1","title":"MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and Enhancement","date":"2018-10-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/enhancing-the-spatial-resolution-of-stereo","title":"Enhancing the Spatial Resolution of Stereo Images Using a Parallax Prior","date":"2018-06-01","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/video-enhancement-with-task-oriented-flow","title":"Video Enhancement with Task-Oriented Flow","date":"2017-11-24","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/video-frame-interpolation-via-adaptive","title":"Video Frame Interpolation via Adaptive Separable Convolution","date":"2017-08-05","rows_on_this_dataset":1,"code_links":6,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":6,"samples_harvested":67,"samples_ran":26,"samples_unverified":41,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":2,"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."}