{"url":"/dataset/tum-vie","name":"TUM-VIE","full_name":"TUM Stereo Visual-Inertial Event Dataset","description_markdown":"**TUM-VIE** is an event camera dataset for developing 3D perception and navigation algorithms. It contains handheld and head-mounted sequences in indoor and outdoor environments with rapid motion during sports and high dynamic range. TUM-VIE includes challenging sequences where state-of-the art VIO fails or results in large drift. Hence, it can help to push the boundary on event-based visual-inertial algorithms.","description_withheld":null,"homepage":"https://vision.in.tum.de/data/datasets/visual-inertial-event-dataset","introduced_date":"2021-08-16","introduced_date_note":null,"introduced_by":{"paper":"/paper/tum-vie-the-tum-stereo-visual-inertial-event","title":"TUM-VIE: The TUM Stereo Visual-Inertial Event Dataset","first_author":"Simon Klenk","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[{"name":"Event-based vision","url":"/task/event-based-vision","datasets_with_task":"/datasets/task/event-based-vision"}],"languages":[],"variants":["TUM-VIE"],"data_loaders":[],"num_papers_in_archive":18,"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."}