{"url":"/dataset/cope-119","name":"COPE-119","full_name":null,"description_markdown":"DynOPETs is a real-world RGB-D dataset designed for object pose estimation and tracking in dynamic scenes with moving cameras.\r\nCOPE119\r\n119 sequences covering 6 common categories from the COPE benchmark: bottles, bowls, cameras, cans, laptops, mugs. Designed for COPE (Category-level Pose Estimation) methods.","description_withheld":null,"homepage":"https://stay332.github.io/DynOPETs/","introduced_date":"2025-03-25","introduced_date_note":null,"introduced_by":{"paper":"/paper/dynopets-a-versatile-benchmark-for-dynamic","title":"DynOPETs: A Versatile Benchmark for Dynamic Object Pose Estimation and Tracking in Moving Camera Scenarios","first_author":"Xiangting Meng","url":null},"license":{"name":"CC BY-NC-SA 4.0","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"modalities":[],"tasks":[{"name":"Pose Estimation","url":"/task/pose-estimation","datasets_with_task":"/datasets/task/pose-estimation"},{"name":"Object Tracking","url":"/task/object-tracking","datasets_with_task":"/datasets/task/object-tracking"},{"name":"Pose Tracking","url":"/task/pose-tracking","datasets_with_task":"/datasets/task/pose-tracking"}],"languages":[],"variants":["COPE-119"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}