{"url":"/dataset/tum-rgb-d","name":"TUM RGB-D","full_name":"TUM RGB-D","description_markdown":"**TUM RGB-D** is an RGB-D dataset. It contains the color and depth images of a Microsoft Kinect sensor along the ground-truth trajectory of the sensor. The data was recorded at full frame rate (30 Hz) and sensor resolution (640x480). The ground-truth trajectory was obtained from a high-accuracy motion-capture system with eight high-speed tracking cameras (100 Hz).\r\n\r\nSource: [https://vision.in.tum.de/data/datasets/rgbd-dataset](https://vision.in.tum.de/data/datasets/rgbd-dataset)\r\nImage Source: [https://vision.in.tum.de/research/rgb-d_sensors_kinect](https://vision.in.tum.de/research/rgb-d_sensors_kinect)","description_withheld":null,"homepage":"https://vision.in.tum.de/data/datasets/rgbd-dataset","introduced_date":"2012-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"A benchmark for the evaluation of RGB-D SLAM systems","first_author":null,"url":"https://doi.org/10.1109/IROS.2012.6385773"},"license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"RGB-D","url":"/datasets/modality/rgb-d"}],"tasks":[{"name":"Depth Estimation","url":"/task/depth-estimation","datasets_with_task":"/datasets/task/depth-estimation"},{"name":"Visual Odometry","url":"/task/visual-odometry","datasets_with_task":"/datasets/task/visual-odometry"},{"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":["TUM RGB-D"],"data_loaders":[],"num_papers_in_archive":235,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/semantic-slam-on-tum-rgb-d","task":"Semantic SLAM","dataset_variant":"TUM RGB-D","rows":1,"metrics":["ATE"],"first_row_in_archive_order":{"model":"GeneA-SLAM2 f3/w/static","paper":"/paper/genea-slam2-dynamic-slam-with-autoencoder","metrics":{"ATE":"0.007"},"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."}