{"url":"/dataset/real275","name":"REAL275","full_name":"NOCS-REAL275","description_markdown":"REAL275 is a benchmark for category-level pose estimation. It contains 4300 training frames, 950 validation and 2750 for testing across 18 different real scenes.","description_withheld":null,"homepage":"https://geometry.stanford.edu/projects/NOCS_CVPR2019/","introduced_date":"2019-01-09","introduced_date_note":null,"introduced_by":{"paper":"/paper/normalized-object-coordinate-space-for","title":"Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation","first_author":"He Wang","url":null},"license":null,"modalities":[{"name":"RGB-D","url":"/datasets/modality/rgb-d"}],"tasks":[{"name":"6D Pose Estimation","url":"/task/6d-pose-estimation-1","datasets_with_task":"/datasets/task/6d-pose-estimation-1"},{"name":"6D Pose Estimation using RGBD","url":"/task/6d-pose-estimation-using-rgbd","datasets_with_task":"/datasets/task/6d-pose-estimation-using-rgbd"}],"languages":[],"variants":["REAL275"],"data_loaders":[],"num_papers_in_archive":67,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/6d-pose-estimation-using-rgbd-on-real275","task":"6D Pose Estimation using RGBD","dataset_variant":"REAL275","rows":11,"metrics":["mAP 10, 5cm","mAP 10, 10cm","mAP 3DIou@25","mAP 3DIou@50","mAP 5, 5cm","mAP 3DIou@75","mAP 5, 2cm","FPS","Rerr","Terr","mAP 10, 2cm","mAP 15, 5cm"],"first_row_in_archive_order":{"model":"GenPose https://github.com/Jiyao06/GenPose","paper":"/paper/genpose-generative-category-level-object-pose","metrics":{"mAP 10, 2cm":"72.4","mAP 10, 5cm":"84.0","mAP 5, 2cm":"52.1","mAP 5, 5cm":"60.9"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/genpose-generative-category-level-object-pose","title":"GenPose: Generative Category-level Object Pose Estimation via Diffusion Models","date":"2023-06-18","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/generative-category-level-shape-and-pose","title":"Generative Category-Level Shape and Pose Estimation with Semantic Primitives","date":"2022-10-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/gpv-pose-category-level-object-pose","title":"GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting","date":"2022-03-15","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":0,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/cppf-towards-robust-category-level-9d-pose","title":"CPPF: Towards Robust Category-Level 9D Pose Estimation in the Wild","date":"2022-03-07","rows_on_this_dataset":1,"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/centersnap-single-shot-multi-object-3d-shape","title":"CenterSnap: Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation","date":"2022-03-03","rows_on_this_dataset":1,"code_links":3,"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/uda-cope-unsupervised-domain-adaptation-for","title":"UDA-COPE: Unsupervised Domain Adaptation for Category-level Object Pose Estimation","date":"2021-11-24","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/bundletrack-6d-pose-tracking-for-novel","title":"BundleTrack: 6D Pose Tracking for Novel Objects without Instance or Category-Level 3D Models","date":"2021-08-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/fs-net-fast-shape-based-network-for-category","title":"FS-Net: Fast Shape-based Network for Category-Level 6D Object Pose Estimation with Decoupled Rotation Mechanism","date":"2021-03-12","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":7,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/dualposenet-category-level-6d-object-pose-and","title":"DualPoseNet: Category-level 6D Object Pose and Size Estimation Using Dual Pose Network with Refined Learning of Pose Consistency","date":"2021-03-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/6-pack-category-level-6d-pose-tracker-with","title":"6-PACK: Category-level 6D Pose Tracker with Anchor-Based Keypoints","date":"2019-10-23","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":1,"samples_unverified":14,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/normalized-object-coordinate-space-for","title":"Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation","date":"2019-01-09","rows_on_this_dataset":1,"code_links":10,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":4,"samples_unverified":16,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":6,"samples_harvested":60,"samples_ran":13,"samples_unverified":47,"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."}