{"url":"/dataset/rope3d","name":"Rope3D","full_name":null,"description_markdown":"**Roadside Perception 3D** (**Rope3D**) is a dataset for autonomous driving and monocular 3D object detection task consisting of 50k images and over 1.5M 3D objects in various scenes, which are captured under different settings including various cameras with ambiguous mounting positions, camera specifications, viewpoints, and different environmental conditions.","description_withheld":null,"homepage":"https://thudair.baai.ac.cn/rope","introduced_date":"2022-03-25","introduced_date_note":null,"introduced_by":{"paper":"/paper/rope3d-theroadside-perception-dataset-for","title":"Rope3D: TheRoadside Perception Dataset for Autonomous Driving and Monocular 3D Object Detection Task","first_author":"Xiaoqing Ye","url":null},"license":null,"modalities":[],"tasks":[{"name":"3D Object Detection","url":"/task/3d-object-detection","datasets_with_task":"/datasets/task/3d-object-detection"}],"languages":[],"variants":["Rope3D"],"data_loaders":[],"num_papers_in_archive":9,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-object-detection-on-rope3d","task":"3D Object Detection","dataset_variant":"Rope3D","rows":8,"metrics":["AP@0.7"],"first_row_in_archive_order":{"model":"MonoUNI","paper":"/paper/monouni-a-unified-vehicle-and-infrastructure","metrics":{"AP@0.7":"75.27"},"code_links":[{"title":"Traffic-X/MonoUNI","url":"https://github.com/Traffic-X/MonoUNI"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/cobev-elevating-roadside-3d-object-detection","title":"CoBEV: Elevating Roadside 3D Object Detection with Depth and Height Complementarity","date":"2023-10-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/monouni-a-unified-vehicle-and-infrastructure","title":"MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues","date":"2023-09-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/bevheight-a-robust-framework-for-vision-based","title":"BEVHeight: A Robust Framework for Vision-based Roadside 3D Object Detection","date":"2023-03-15","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":12,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/bevformer-v2-adapting-modern-image-backbones","title":"BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision","date":"2022-11-18","rows_on_this_dataset":1,"code_links":2,"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/bevdepth-acquisition-of-reliable-depth-for","title":"BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection","date":"2022-06-21","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/delving-into-localization-errors-for","title":"Delving into Localization Errors for Monocular 3D Object Detection","date":"2021-03-30","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/kinematic-3d-object-detection-in-monocular","title":"Kinematic 3D Object Detection in Monocular Video","date":"2020-07-19","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":3,"samples_unverified":17,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/m3d-rpn-monocular-3d-region-proposal-network","title":"M3D-RPN: Monocular 3D Region Proposal Network for Object Detection","date":"2019-07-13","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":21,"samples_ran":1,"samples_unverified":20,"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":5,"samples_harvested":58,"samples_ran":17,"samples_unverified":41,"pointer_only_for_licence":0,"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."}