{"url":"/dataset/dtu","name":"DTU","full_name":"DTU MVS dataset - 2014","description_markdown":"DTU MVS 2014 is a multi-view stereo dataset, which is an order of magnitude larger in number of scenes and with a significant increase in diversity. Specifically, it contains 80 scenes of large variability. Each scene consists of 49 or 64 accurate camera positions and reference structured light scans, all acquired by a 6-axis industrial robot.","description_withheld":null,"homepage":"http://roboimagedata.compute.dtu.dk/?page_id=36","introduced_date":"2014-06-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/large-scale-multi-view-stereopsis-evaluation","title":"Large Scale Multi-view Stereopsis Evaluation","first_author":"Rasmus Jensen","url":null},"license":{"name":"Free","url":null},"modalities":[],"tasks":[{"name":"3D Reconstruction","url":"/task/3d-reconstruction","datasets_with_task":"/datasets/task/3d-reconstruction"},{"name":"Point Clouds","url":"/task/point-clouds","datasets_with_task":"/datasets/task/point-clouds"}],"languages":[],"variants":["DTU"],"data_loaders":[],"num_papers_in_archive":313,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-reconstruction-on-dtu","task":"3D Reconstruction","dataset_variant":"DTU","rows":24,"metrics":["Overall","Acc","Comp"],"first_row_in_archive_order":{"model":"MVSFormer++","paper":"/paper/mvsformer-revealing-the-devil-in-transformer","metrics":{"Acc":"0.3090","Comp":"0.2521","Overall":"0.2805"},"code_links":[{"title":"maybelx/mvsformerplusplus","url":"https://github.com/maybelx/mvsformerplusplus"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/point-clouds-on-dtu","task":"Point Clouds","dataset_variant":"DTU","rows":1,"metrics":["Overall"],"first_row_in_archive_order":{"model":"Vis-MVSNet","paper":"/paper/visibility-aware-multi-view-stereo-network","metrics":{"Overall":"0.365"},"code_links":[{"title":"jzhangbs/Vis-MVSNet","url":"https://github.com/jzhangbs/Vis-MVSNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/gomvs-geometrically-consistent-cost","title":"GoMVS: Geometrically Consistent Cost Aggregation for Multi-View Stereo","date":"2024-04-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":0,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mvsformer-revealing-the-devil-in-transformer","title":"MVSFormer++: Revealing the Devil in Transformer's Details for Multi-View Stereo","date":"2024-01-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":19,"samples_ran":15,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/gc-mvsnet-multi-view-multi-scale","title":"GC-MVSNet: Multi-View, Multi-Scale, Geometrically-Consistent Multi-View Stereo","date":"2023-10-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/when-epipolar-constraint-meets-non-local-1","title":"When Epipolar Constraint Meets Non-local Operators in Multi-View Stereo","date":"2023-09-29","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":5,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multi-view-stereo-representation-revist","title":"Multi-View Stereo Representation Revisit: Region-Aware MVSNet","date":"2023-04-26","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/geomvsnet-learning-multi-view-stereo-with","title":"GeoMVSNet: Learning Multi-View Stereo With Geometry Perception","date":"2023-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/mvsformer-learning-robust-image","title":"MVSFormer: Multi-View Stereo by Learning Robust Image Features and Temperature-based Depth","date":"2022-08-04","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":12,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/cost-volume-pyramid-network-with-multi","title":"Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view Stereo","date":"2022-07-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rethinking-depth-estimation-for-multi-view","title":"Rethinking Depth Estimation for Multi-View Stereo: A Unified Representation","date":"2022-01-05","rows_on_this_dataset":1,"code_links":1,"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/curvature-guided-dynamic-scale-networks-for-1","title":"Curvature-guided dynamic scale networks for Multi-view Stereo","date":"2021-12-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":18,"samples_ran":16,"samples_unverified":2,"pointer_only_for_licence":18,"claim":"Per-sample execution on synthesized fixtures; 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not a correctness claim."}},{"paper":"/paper/deep-stereo-using-adaptive-thin-volume","title":"Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awareness","date":"2019-11-27","rows_on_this_dataset":1,"code_links":1,"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/mvsnet-depth-inference-for-unstructured-multi","title":"MVSNet: Depth Inference for Unstructured Multi-view Stereo","date":"2018-04-07","rows_on_this_dataset":1,"code_links":5,"syntology":null},{"paper":"/paper/structure-from-motion-revisited","title":"Structure-From-Motion Revisited","date":"2016-06-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/3d-r2n2-a-unified-approach-for-single-and","title":"3D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstruction","date":"2016-04-02","rows_on_this_dataset":1,"code_links":13,"syntology":null},{"paper":"/paper/massively-parallel-multiview-stereopsis-by","title":"Massively Parallel Multiview Stereopsis by Surface Normal Diffusion","date":"2015-12-01","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":10,"samples_harvested":135,"samples_ran":64,"samples_unverified":71,"pointer_only_for_licence":18,"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."}