{"url":"/dataset/ycb-video","name":"YCB-Video","full_name":null,"description_markdown":"The **YCB-Video** dataset is a large-scale video dataset for 6D object pose estimation. provides accurate 6D poses of 21 objects from the YCB dataset observed in 92 videos with 133,827 frames.\r\n\r\nSource: [https://rse-lab.cs.washington.edu/projects/posecnn/](https://rse-lab.cs.washington.edu/projects/posecnn/)\r\nImage Source: [https://www.researchgate.net/figure/Examples-of-refined-poses-on-the-YCB-Video-dataset-which-use-results-from-PoseCNN-Xiang_fig6_339663565](https://www.researchgate.net/figure/Examples-of-refined-poses-on-the-YCB-Video-dataset-which-use-results-from-PoseCNN-Xiang_fig6_339663565)","description_withheld":null,"homepage":"https://rse-lab.cs.washington.edu/projects/posecnn/","introduced_date":"2018-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/posecnn-a-convolutional-neural-network-for-6d","title":"PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes","first_author":"Yu Xiang","url":null},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"},{"name":"RGB-D","url":"/datasets/modality/rgb-d"}],"tasks":[{"name":"Pose Estimation","url":"/task/pose-estimation","datasets_with_task":"/datasets/task/pose-estimation"},{"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"},{"name":"6D Pose Estimation using RGB","url":"/task/6d-pose-estimation","datasets_with_task":"/datasets/task/6d-pose-estimation"},{"name":"Occluded 3D Object Symmetry Detection","url":"/task/occluded-3d-object-symmetry-detection","datasets_with_task":"/datasets/task/occluded-3d-object-symmetry-detection"},{"name":"Symmetry Detection","url":"/task/symmetry-detection","datasets_with_task":"/datasets/task/symmetry-detection"}],"languages":[],"variants":["YCB-Video"],"data_loaders":[],"num_papers_in_archive":164,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/6d-pose-estimation-on-ycb-video-2","task":"6D Pose Estimation","dataset_variant":"YCB-Video","rows":10,"metrics":["ADDS AUC"],"first_row_in_archive_order":{"model":"ICG+","paper":"/paper/fusing-visual-appearance-and-geometry-for","metrics":{"ADDS AUC":"97.9"},"code_links":[{"title":"dlr-rm/3dobjecttracking","url":"https://github.com/dlr-rm/3dobjecttracking"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/6d-pose-estimation-using-rgbd-on-ycb-video","task":"6D Pose Estimation using RGBD","dataset_variant":"YCB-Video","rows":9,"metrics":["Mean ADD","Mean ADD-S","Mean ADI","ADD(S) AUC","ADD-S AUC","ADD-S (2cm)"],"first_row_in_archive_order":{"model":"CMCL6D","paper":"/paper/enhancing-6-dof-object-pose-estimation","metrics":{"Mean ADD":"95.43","Mean ADD-S":"95.43"},"code_links":[{"title":"wangzihanggg/CMCL6D","url":"https://github.com/wangzihanggg/CMCL6D"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/6d-pose-estimation-on-ycb-video","task":"6D Pose Estimation using RGB","dataset_variant":"YCB-Video","rows":5,"metrics":["Mean ADD","Accuracy (ADD)","Mean ADD-S","Mean AUC","Mean ADI"],"first_row_in_archive_order":{"model":"PoET","paper":"/paper/poet-pose-estimation-transformer-for-single","metrics":{"Mean ADD":"70.1","Mean ADD-S":"74.9","Mean ADI":"87.1"},"code_links":[{"title":"aau-cns/poet","url":"https://github.com/aau-cns/poet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/occluded-3d-object-symmetry-detection-on-ycb","task":"Occluded 3D Object Symmetry Detection","dataset_variant":"YCB-Video","rows":1,"metrics":["PR AUC"],"first_row_in_archive_order":{"model":"(DOSE)Dense Occlusion Symmetry Network","paper":"/paper/symmetry-detection-of-occluded-point-cloud","metrics":{"PR AUC":"0.516"},"code_links":[{"title":"Allen--Wu/Symmetry-Detection-of-Occluded-Point-Cloud-Using-Deep-Learning","url":"https://github.com/Allen--Wu/Symmetry-Detection-of-Occluded-Point-Cloud-Using-Deep-Learning"},{"title":"Allen--Wu/dense_symmetry","url":"https://github.com/Allen--Wu/dense_symmetry"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/symmetry-detection-on-ycb-video","task":"Symmetry Detection","dataset_variant":"YCB-Video","rows":1,"metrics":["PR AUC"],"first_row_in_archive_order":{"model":"(DOSE)Dense Occlusion Symmetry Network","paper":"/paper/symmetry-detection-of-occluded-point-cloud","metrics":{"PR AUC":"0.516"},"code_links":[{"title":"Allen--Wu/Symmetry-Detection-of-Occluded-Point-Cloud-Using-Deep-Learning","url":"https://github.com/Allen--Wu/Symmetry-Detection-of-Occluded-Point-Cloud-Using-Deep-Learning"},{"title":"Allen--Wu/dense_symmetry","url":"https://github.com/Allen--Wu/dense_symmetry"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/towards-subcentimeter-accuracy-digital-twin","title":"Robust 6DoF Pose Estimation Against Depth Noise and a Comprehensive Evaluation on a Mobile Dataset","date":"2023-09-24","rows_on_this_dataset":2,"code_links":3,"syntology":null},{"paper":"/paper/enhancing-6-dof-object-pose-estimation","title":"Enhancing 6-DoF Object Pose Estimation through Multiple Modality Fusion: A Hybrid CNN Architecture with Cross-Layer and Cross-Modal Integration","date":"2023-09-06","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/fusing-visual-appearance-and-geometry-for","title":"Fusing Visual Appearance and Geometry for Multi-modality 6DoF Object Tracking","date":"2023-02-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/poet-pose-estimation-transformer-for-single","title":"PoET: Pose Estimation Transformer for Single-View, Multi-Object 6D Pose Estimation","date":"2022-11-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/iterative-corresponding-geometry-fusing","title":"Iterative Corresponding Geometry: Fusing Region and Depth for Highly Efficient 3D Tracking of Textureless Objects","date":"2022-03-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/occlusion-robust-object-pose-estimation-with","title":"Occlusion-Robust Object Pose Estimation with Holistic Representation","date":"2021-10-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/vote-from-the-center-6-dof-pose-estimation-in","title":"Vote from the Center: 6 DoF Pose Estimation in RGB-D Images by Radial Keypoint Voting","date":"2021-04-06","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/ffb6d-a-full-flow-bidirectional-fusion","title":"FFB6D: A Full Flow Bidirectional Fusion Network for 6D Pose Estimation","date":"2021-03-03","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":1,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/se-3-tracknet-data-driven-6d-pose-tracking-by","title":"se(3)-TrackNet: Data-driven 6D Pose Tracking by Calibrating Image Residuals in Synthetic Domains","date":"2020-07-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/symmetry-detection-of-occluded-point-cloud","title":"Symmetry Detection of Occluded Point Cloud Using Deep Learning","date":"2020-03-14","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/maskedfusion-mask-based-6d-object-pose","title":"MaskedFusion: Mask-based 6D Object Pose Estimation","date":"2019-11-18","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/pvn3d-a-deep-point-wise-3d-keypoints-voting","title":"PVN3D: A Deep Point-wise 3D Keypoints Voting Network for 6DoF Pose Estimation","date":"2019-11-11","rows_on_this_dataset":2,"code_links":3,"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/densefusion-6d-object-pose-estimation-by","title":"DenseFusion: 6D Object Pose Estimation by Iterative Dense Fusion","date":"2019-01-15","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":19,"samples_ran":1,"samples_unverified":18,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pvnet-pixel-wise-voting-network-for-6dof-pose","title":"PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation","date":"2018-12-31","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":2,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/segmentation-driven-6d-object-pose-estimation","title":"Segmentation-driven 6D Object Pose Estimation","date":"2018-12-06","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":0,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deepim-deep-iterative-matching-for-6d-pose","title":"DeepIM: Deep Iterative Matching for 6D Pose Estimation","date":"2018-03-31","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/posecnn-a-convolutional-neural-network-for-6d","title":"PoseCNN: A Convolutional Neural Network for 6D Object Pose Estimation in Cluttered Scenes","date":"2017-11-01","rows_on_this_dataset":4,"code_links":12,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":1,"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":7,"samples_harvested":52,"samples_ran":7,"samples_unverified":45,"pointer_only_for_licence":1,"papers_with_no_sample_that_ran":3,"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."}