{"url":"/dataset/bonn-rgb-d-dynamic","name":"Bonn RGB-D Dynamic","full_name":null,"description_markdown":"**Bonn RGB-D Dynamic** is a dataset for RGB-D SLAM, containing highly dynamic sequences. We provide 24 dynamic sequences, where people perform different tasks, such as manipulating boxes or playing with balloons, plus 2 static sequences. For each scene we provide the ground truth pose of the sensor, recorded with an Optitrack Prime 13 motion capture system. The sequences are in the same format as the TUM RGB-D Dataset, so that the same evaluation tools can be used. Furthermore, we provide a ground truth 3D point cloud of the static environment recorded using a Leica BLK360 terrestrial laser scanner.","description_withheld":null,"homepage":"http://www.ipb.uni-bonn.de/data/rgbd-dynamic-dataset/","introduced_date":"2019-05-06","introduced_date_note":null,"introduced_by":{"paper":"/paper/190502082","title":"ReFusion: 3D Reconstruction in Dynamic Environments for RGB-D Cameras Exploiting Residuals","first_author":null,"url":null},"license":null,"modalities":[{"name":"RGB-D","url":"/datasets/modality/rgb-d"}],"tasks":[{"name":"Simultaneous Localization and Mapping","url":"/task/simultaneous-localization-and-mapping","datasets_with_task":"/datasets/task/simultaneous-localization-and-mapping"},{"name":"Semantic SLAM","url":"/task/semantic-slam","datasets_with_task":"/datasets/task/semantic-slam"}],"languages":[],"variants":["Bonn RGB-D Dynamic"],"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/semantic-slam-on-bonn-rgb-d-dynamic","task":"Semantic SLAM","dataset_variant":"Bonn RGB-D Dynamic","rows":1,"metrics":["ATE"],"first_row_in_archive_order":{"model":"GeneA-SLAM2 synchronous2","paper":"/paper/genea-slam2-dynamic-slam-with-autoencoder","metrics":{"ATE":"0.008"},"code_links":[{"title":"qingshufan/GeneA-SLAM2","url":"https://github.com/qingshufan/GeneA-SLAM2"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/genea-slam2-dynamic-slam-with-autoencoder","title":"GeneA-SLAM2: Dynamic SLAM with AutoEncoder-Preprocessed Genetic Keypoints Resampling and Depth Variance-Guided Dynamic Region Removal","date":"2025-06-03","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"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."}