{"url":"/dataset/mvsec","name":"MVSEC","full_name":"Multi Vehicle Stereo Event Camera","description_markdown":"The Multi Vehicle Stereo Event Camera (MVSEC) dataset is a collection of data designed for the development of novel 3D perception algorithms for event based cameras. Stereo event data is collected from car, motorbike, hexacopter and handheld data, and fused with lidar, IMU, motion capture and GPS to provide ground truth pose and depth images. \r\n\r\nSource: [EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras](/paper/ev-flownet-self-supervised-optical-flow)","description_withheld":null,"homepage":"https://daniilidis-group.github.io/mvsec/","introduced_date":"2018-01-30","introduced_date_note":null,"introduced_by":{"paper":null,"title":"The Multi Vehicle Stereo Event Camera Dataset: An Event Camera Dataset for 3D Perception","first_author":null,"url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Stereo","url":"/datasets/modality/stereo"},{"name":"LiDAR","url":"/datasets/modality/lidar"}],"tasks":[{"name":"Optical Flow Estimation","url":"/task/optical-flow-estimation","datasets_with_task":"/datasets/task/optical-flow-estimation"},{"name":"Self-Supervised Learning","url":"/task/self-supervised-learning","datasets_with_task":"/datasets/task/self-supervised-learning"},{"name":"Video Reconstruction","url":"/task/video-reconstruction","datasets_with_task":"/datasets/task/video-reconstruction"},{"name":"Event-based Optical Flow","url":"/task/event-based-optical-flow","datasets_with_task":"/datasets/task/event-based-optical-flow"}],"languages":[],"variants":["MVSEC"],"data_loaders":[],"num_papers_in_archive":28,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/video-reconstruction-on-mvsec","task":"Video Reconstruction","dataset_variant":"MVSEC","rows":4,"metrics":["Mean Squared Error","LPIPS"],"first_row_in_archive_order":{"model":"HyperE2VID","paper":"/paper/hypere2vid-improving-event-based-video","metrics":{"LPIPS":"0.476","Mean Squared Error":"0.076"},"code_links":[{"title":"ercanburak/HyperE2VID","url":"https://github.com/ercanburak/HyperE2VID"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/event-based-optical-flow-on-mvsec","task":"Event-based Optical Flow","dataset_variant":"MVSEC","rows":1,"metrics":["Average End-Point Error"],"first_row_in_archive_order":{"model":"S-TLLR","paper":"/paper/s-tllr-stdp-inspired-temporal-local-learning","metrics":{"Average End-Point Error":"3.45"},"code_links":[{"title":"mapolinario94/s-tllr","url":"https://github.com/mapolinario94/s-tllr"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/s-tllr-stdp-inspired-temporal-local-learning","title":"S-TLLR: STDP-inspired Temporal Local Learning Rule for Spiking Neural Networks","date":"2023-06-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hypere2vid-improving-event-based-video","title":"HyperE2VID: Improving Event-Based Video Reconstruction via Hypernetworks","date":"2023-05-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/event-based-video-reconstruction-using","title":"Event-Based Video Reconstruction Using Transformer","date":"2021-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/how-to-train-your-event-camera-neural-network","title":"Reducing the Sim-to-Real Gap for Event Cameras","date":"2020-03-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/high-speed-and-high-dynamic-range-video-with","title":"High Speed and High Dynamic Range Video with an Event Camera","date":"2019-06-15","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":2,"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":1,"samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":2,"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."}