{"url":"/dataset/faust-partial","name":"FPv1","full_name":null,"description_markdown":"FPv1 (prior name FAUST-partial) is a 3D registration benchmark dataset created to address the lack of data variability in the existing 3D registration benchmarks such as: 3DMatch, ETH, KITTI.\r\n\r\nThe original FAUST training dataset is comprised of 100 3D scans of human bodies.\r\n\r\nThe benchmark generation for a single scan from the FAUST training dataset can be summarized as follows:\r\n\r\n1. Make xz-plane the floor by translating the minimal bounding box point of the scan to the origin \r\n2. Surround the scan with a regular icosahaedron. Each point of the icosahaedron acts as a viewpoint\r\n3. For each viewpoint, create a partial point cloud using the hidden point removal algorithm\r\n\r\nFinally, for a pair of partial point clouds with the desired overalp, generate a random rotation from the desired rotation range and translation range.","description_withheld":null,"homepage":"https://github.com/DavidBoja/FPv1","introduced_date":"2022-11-29","introduced_date_note":null,"introduced_by":{"paper":"/paper/challenging-the-universal-representation-of","title":"Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration","first_author":"David Bojanić","url":null},"license":null,"modalities":[{"name":"3D","url":"/datasets/modality/3d"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"}],"tasks":[{"name":"Point Cloud Registration","url":"/task/point-cloud-registration","datasets_with_task":"/datasets/task/point-cloud-registration"}],"languages":[],"variants":["FPv1","FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50)","FAUST-partial (60%+ overlap, Rot 0-45, Trans -50-50, trained on 3DMatch)"],"data_loaders":[{"repo":"https://github.com/DavidBoja/FAUST-partial","url":"https://github.com/DavidBoja/FAUST-partial","frameworks":[]}],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/point-cloud-registration-on-fpv1","task":"Point Cloud Registration","dataset_variant":"FPv1","rows":8,"metrics":["Recall (3cm, 10 degrees)","RRE (degrees)","RTE (cm)"],"first_row_in_archive_order":{"model":"Greedy Grid Search","paper":"/paper/challenging-the-universal-representation-of","metrics":{"RRE (degrees)":"0.014","RTE (cm)":"0.009","Recall (3cm, 10 degrees)":"92.81"},"code_links":[{"title":"davidboja/greedy-grid-search","url":"https://github.com/davidboja/greedy-grid-search"},{"title":"DavidBoja/FAUST-partial","url":"https://github.com/DavidBoja/FAUST-partial"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/challenging-the-universal-representation-of","title":"Challenging the Universal Representation of Deep Models for 3D Point Cloud Registration","date":"2022-11-29","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/geometric-transformer-for-fast-and-robust","title":"Geometric Transformer for Fast and Robust Point Cloud Registration","date":"2022-02-14","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":4,"samples_unverified":5,"pointer_only_for_licence":9,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/you-only-hypothesize-once-point-cloud","title":"You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors","date":"2021-09-01","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/pointdsc-robust-point-cloud-registration","title":"PointDSC: Robust Point Cloud Registration using Deep Spatial Consistency","date":"2021-03-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/spinnet-learning-a-general-surface-descriptor","title":"SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration","date":"2020-11-24","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/distinctive-3d-local-deep-descriptors","title":"Distinctive 3D local deep descriptors","date":"2020-09-01","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/fast-point-feature-histograms-fpfh-for-3d","title":"Fast Point Feature Histograms (FPFH) for 3D Registration","date":"2009-05-12","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":13,"samples_ran":6,"samples_unverified":7,"pointer_only_for_licence":12,"papers_with_no_sample_that_ran":1,"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."}