{"url":"/dataset/hpatches","name":"HPatches","full_name":"Homography-patches dataset","description_markdown":"The **HPatches** is a recent dataset for local patch descriptor evaluation that consists of 116 sequences of 6 images with known homography. The dataset is split into two parts: viewpoint - 59 sequences with significant viewpoint change and illumination - 57 sequences with significant illumination change, both natural and artificial.\r\n\r\nSource: [RF-Net: An End-to-End Image Matching Network based on Receptive Field](https://arxiv.org/abs/1906.00604)\r\nImage Source: [https://www.robots.ox.ac.uk/~vgg/publications/2017/Balntas17/balntas17.pdf](https://www.robots.ox.ac.uk/~vgg/publications/2017/Balntas17/balntas17.pdf)","description_withheld":null,"homepage":"https://github.com/hpatches/hpatches-dataset","introduced_date":"2017-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/hpatches-a-benchmark-and-evaluation-of","title":"HPatches: A benchmark and evaluation of handcrafted and learned local descriptors","first_author":"Vassileios Balntas","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Dense Pixel Correspondence Estimation","url":"/task/dense-pixel-correspondence-estimation","datasets_with_task":"/datasets/task/dense-pixel-correspondence-estimation"},{"name":"Patch Matching","url":"/task/patch-matching","datasets_with_task":"/datasets/task/patch-matching"},{"name":"Image Stitching","url":"/task/image-stitching","datasets_with_task":"/datasets/task/image-stitching"},{"name":"Geometric Matching","url":"/task/geometric-matching","datasets_with_task":"/datasets/task/geometric-matching"}],"languages":[],"variants":["HPatches"],"data_loaders":[{"repo":"https://github.com/hpatches/hpatches-dataset","url":"https://github.com/hpatches/hpatches-dataset","frameworks":[]}],"num_papers_in_archive":248,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/dense-pixel-correspondence-estimation-on","task":"Dense Pixel Correspondence Estimation","dataset_variant":"HPatches","rows":8,"metrics":["Viewpoint I AEPE","Viewpoint II AEPE","Viewpoint III AEPE","Viewpoint IV AEPE","Viewpoint V AEPE","PCK-5px","PCK-1px","PCK-3px"],"first_row_in_archive_order":{"model":"RANSAC-DMP+","paper":"/paper/deep-matching-prior-test-time-optimization","metrics":{"PCK-5px":"97.52","Viewpoint I AEPE":"0.48","Viewpoint II AEPE":"2.24","Viewpoint III AEPE":"2.41","Viewpoint IV AEPE":"4.32","Viewpoint V AEPE":"5.16"},"code_links":[{"title":"SunghwanHong/Deep-Matching-Prior","url":"https://github.com/SunghwanHong/Deep-Matching-Prior"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/patch-matching-on-hpatches","task":"Patch Matching","dataset_variant":"HPatches","rows":2,"metrics":["Patch Matching","Patch Retrieval","Patch Verification"],"first_row_in_archive_order":{"model":"Twin-Net","paper":"/paper/twin-net-descriptor-twin-negative-mining-with","metrics":{"Patch Matching":"53.95","Patch Retrieval":"71.66","Patch Verification":"89.06"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/geometric-matching-on-hpatches","task":"Geometric Matching","dataset_variant":"HPatches","rows":1,"metrics":["Average End-Point Error"],"first_row_in_archive_order":{"model":"IFCAT (Ours)","paper":"/paper/integrative-feature-and-cost-aggregation-with","metrics":{"Average End-Point Error":"17.59"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-stitching-on-hpatches","task":"Image Stitching","dataset_variant":"HPatches","rows":1,"metrics":["0..5sec"],"first_row_in_archive_order":{"model":"IF-Net","paper":"/paper/if-net-an-illumination-invariant-feature","metrics":{"0..5sec":"10000"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/integrative-feature-and-cost-aggregation-with","title":"Integrative Feature and Cost Aggregation with Transformers for Dense Correspondence","date":"2022-09-19","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-matching-prior-test-time-optimization","title":"Deep Matching Prior: Test-Time Optimization for Dense Correspondence","date":"2021-06-06","rows_on_this_dataset":1,"code_links":1,"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/if-net-an-illumination-invariant-feature","title":"IF-Net: An Illumination-invariant Feature Network","date":"2020-08-10","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/landscapear-large-scale-outdoor-augmented","title":"LandscapeAR: Large Scale Outdoor Augmented Reality by Matching Photographs with Terrain Models Using Learned Descriptors","date":"2020-08-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/twin-net-descriptor-twin-negative-mining-with","title":"Twin-Net Descriptor: Twin Negative Mining With Quad Loss for Patch-Based Matching","date":"2019-09-19","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/dgc-net-dense-geometric-correspondence","title":"DGC-Net: Dense Geometric Correspondence Network","date":"2018-10-19","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/pwc-net-cnns-for-optical-flow-using-pyramid","title":"PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume","date":"2017-09-07","rows_on_this_dataset":1,"code_links":21,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/flownet-20-evolution-of-optical-flow","title":"FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks","date":"2016-12-06","rows_on_this_dataset":1,"code_links":12,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":21,"samples_ran":2,"samples_unverified":19,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/optical-flow-estimation-using-a-spatial","title":"Optical Flow Estimation using a Spatial Pyramid Network","date":"2016-11-03","rows_on_this_dataset":1,"code_links":8,"syntology":null},{"paper":"/paper/deepmatching-hierarchical-deformable-dense","title":"DeepMatching: Hierarchical Deformable Dense Matching","date":"2015-06-25","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":26,"samples_ran":6,"samples_unverified":20,"pointer_only_for_licence":6,"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."}