{"url":"/dataset/eth3d","name":"ETH3D","full_name":null,"description_markdown":"ETHD is a multi-view stereo benchmark / 3D reconstruction benchmark that covers a variety of indoor and outdoor scenes.\r\nGround truth geometry has been obtained using a high-precision laser scanner.\r\nA DSLR camera as well as a synchronized multi-camera rig with varying field-of-view was used to capture images.\r\n\r\nSource: [A Multi-View Stereo Benchmark With High-Resolution Images and Multi-Camera Videos](/paper/a-multi-view-stereo-benchmark-with-high)","description_withheld":null,"homepage":"https://www.eth3d.net/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/a-multi-view-stereo-benchmark-with-high","title":"A Multi-View Stereo Benchmark With High-Resolution Images and Multi-Camera Videos","first_author":"Thomas Schops","url":null},"license":{"name":"CC BY-NC-SA 4.0","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"RGB-D","url":"/datasets/modality/rgb-d"},{"name":"Stereo","url":"/datasets/modality/stereo"}],"tasks":[{"name":"Depth Estimation","url":"/task/depth-estimation","datasets_with_task":"/datasets/task/depth-estimation"},{"name":"3D Reconstruction","url":"/task/3d-reconstruction","datasets_with_task":"/datasets/task/3d-reconstruction"},{"name":"Monocular Depth Estimation","url":"/task/monocular-depth-estimation","datasets_with_task":"/datasets/task/monocular-depth-estimation"},{"name":"Dense Pixel Correspondence Estimation","url":"/task/dense-pixel-correspondence-estimation","datasets_with_task":"/datasets/task/dense-pixel-correspondence-estimation"},{"name":"Multi-View 3D Reconstruction","url":"/task/multi-view-3d-reconstruction","datasets_with_task":"/datasets/task/multi-view-3d-reconstruction"},{"name":"Stereo Matching","url":"/task/stereo-matching-1","datasets_with_task":"/datasets/task/stereo-matching-1"}],"languages":[],"variants":["ETH3D"],"data_loaders":[],"num_papers_in_archive":121,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/monocular-depth-estimation-on-eth3d","task":"Monocular Depth Estimation","dataset_variant":"ETH3D","rows":10,"metrics":["Delta < 1.25","absolute relative error"],"first_row_in_archive_order":{"model":"Distill Any Depth","paper":"/paper/distill-any-depth-distillation-creates-a","metrics":{"Delta < 1.25":"0.981","absolute relative error":"0.054"},"code_links":[{"title":"Westlake-AGI-Lab/Distill-Any-Depth","url":"https://github.com/Westlake-AGI-Lab/Distill-Any-Depth"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multi-view-3d-reconstruction-on-eth3d","task":"Multi-View 3D Reconstruction","dataset_variant":"ETH3D","rows":5,"metrics":["F1 score"],"first_row_in_archive_order":{"model":"DPE-MVS","paper":"/paper/dual-level-precision-edges-guided-multi-view","metrics":{"F1 score":"89.48"},"code_links":[{"title":"ckh0715/DPE-MVS","url":"https://github.com/ckh0715/DPE-MVS"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/dense-pixel-correspondence-estimation-on-3","task":"Dense Pixel Correspondence Estimation","dataset_variant":"ETH3D","rows":2,"metrics":["AEPE (rate=3)","AEPE (rate=5)"],"first_row_in_archive_order":{"model":"COTR","paper":"/paper/cotr-correspondence-transformer-for-matching","metrics":{"AEPE (rate=3)":"1.66"},"code_links":[{"title":"ubc-vision/COTR","url":"https://github.com/ubc-vision/COTR"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/distill-any-depth-distillation-creates-a","title":"Distill Any Depth: Distillation Creates a Stronger Monocular Depth Estimator","date":"2025-02-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/depthmaster-taming-diffusion-models-for","title":"DepthMaster: Taming Diffusion Models for Monocular Depth Estimation","date":"2025-01-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dual-level-precision-edges-guided-multi-view","title":"Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization","date":"2024-12-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/primedepth-efficient-monocular-depth","title":"PrimeDepth: Efficient Monocular Depth Estimation with a Stable Diffusion Preimage","date":"2024-09-13","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":13,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/depth-anything-unleashing-the-power-of-large","title":"Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data","date":"2024-01-19","rows_on_this_dataset":1,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":3,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/repurposing-diffusion-based-image-generators","title":"Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation","date":"2023-12-04","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":26,"samples_ran":14,"samples_unverified":12,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/midas-v3-1-a-model-zoo-for-robust-monocular","title":"MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation","date":"2023-07-26","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/hierarchical-prior-mining-for-non-local-multi","title":"Hierarchical Prior Mining for Non-local Multi-View Stereo","date":"2023-03-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/adaptive-patch-deformation-for-textureless","title":"Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo","date":"2023-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hierarchical-normalization-for-robust","title":"Hierarchical Normalization for Robust Monocular Depth Estimation","date":"2022-10-18","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/cotr-correspondence-transformer-for-matching","title":"COTR: Correspondence Transformer for Matching Across Images","date":"2021-03-25","rows_on_this_dataset":2,"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":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/vision-transformers-for-dense-prediction","title":"Vision Transformers for Dense Prediction","date":"2021-03-24","rows_on_this_dataset":1,"code_links":15,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":116,"samples_ran":54,"samples_unverified":62,"pointer_only_for_licence":15,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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":1,"code_links":1,"syntology":null},{"paper":"/paper/planar-prior-assisted-patchmatch-multi-view","title":"Planar Prior Assisted PatchMatch Multi-View Stereo","date":"2019-12-26","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/towards-robust-monocular-depth-estimation","title":"Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer","date":"2019-07-02","rows_on_this_dataset":1,"code_links":16,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":17,"samples_ran":6,"samples_unverified":11,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/190408103","title":"Multi-Scale Geometric Consistency Guided Multi-View Stereo","date":"2019-04-17","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":8,"samples_harvested":193,"samples_ran":95,"samples_unverified":98,"pointer_only_for_licence":18,"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."}