{"url":"/dataset/dsec","name":"DSEC","full_name":"A Stereo Event Camera Dataset for Driving Scenarios","description_markdown":"DSEC is a stereo camera dataset in driving scenarios that contains data from two monochrome event cameras and two global shutter color cameras in favorable and challenging illumination conditions. In addition, we collect Lidar data and RTK GPS measurements, both hardware synchronized with all camera data. One of the distinctive features of this dataset is the inclusion of VGA-resolution event cameras. Event cameras have received increasing attention for their high temporal resolution and high dynamic range performance. However, due to their novelty, event camera datasets in driving scenarios are rare. This work presents the first high-resolution, large-scale stereo dataset with event cameras.","description_withheld":null,"homepage":"https://dsec.ifi.uzh.ch/","introduced_date":"2022-11-26","introduced_date_note":null,"introduced_by":null,"license":{"name":"MIT license","url":"https://github.com/uzh-rpg/DSEC/blob/main/LICENSE"},"modalities":[],"tasks":[{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Optical Flow Estimation","url":"/task/optical-flow-estimation","datasets_with_task":"/datasets/task/optical-flow-estimation"},{"name":"Event-based Optical Flow","url":"/task/event-based-optical-flow","datasets_with_task":"/datasets/task/event-based-optical-flow"}],"languages":[],"variants":["DSEC"],"data_loaders":[],"num_papers_in_archive":23,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/object-detection-on-dsec","task":"Object Detection","dataset_variant":"DSEC","rows":12,"metrics":["mAP"],"first_row_in_archive_order":{"model":"CAFR","paper":"/paper/embracing-events-and-frames-with-hierarchical","metrics":{"mAP":"38.0"},"code_links":[{"title":"hucaofighting/frn","url":"https://github.com/hucaofighting/frn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/semantic-segmentation-on-dsec","task":"Semantic Segmentation","dataset_variant":"DSEC","rows":9,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"BRENet","paper":"/paper/rethinking-rgb-event-semantic-segmentation","metrics":{"mIoU":"74.94"},"code_links":[{"title":"zyaocoder/BRENet","url":"https://github.com/zyaocoder/BRENet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/rethinking-rgb-event-semantic-segmentation","title":"Rethinking RGB-Event Semantic Segmentation with a Novel Bidirectional Motion-enhanced Event Representation","date":"2025-05-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/embracing-events-and-frames-with-hierarchical","title":"Embracing Events and Frames with Hierarchical Feature Refinement Network for Object Detection","date":"2024-07-17","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+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/delivering-arbitrary-modal-semantic","title":"Delivering Arbitrary-Modal Semantic Segmentation","date":"2023-03-02","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":7,"samples_ran":7,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/halsie-hybrid-approach-to-learning","title":"HALSIE: Hybrid Approach to Learning Segmentation by Simultaneously Exploiting Image and Event Modalities","date":"2022-11-19","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/segnext-rethinking-convolutional-attention","title":"SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation","date":"2022-09-18","rows_on_this_dataset":1,"code_links":5,"syntology":null},{"paper":"/paper/rgb-event-fusion-for-moving-object-detection","title":"RGB-Event Fusion for Moving Object Detection in Autonomous Driving","date":"2022-09-17","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/fusing-event-based-and-rgb-camera-for-robust","title":"Fusing Event-based and RGB camera for Robust Object Detection in Adverse Conditions","date":"2022-03-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ess-learning-event-based-semantic","title":"ESS: Learning Event-based Semantic Segmentation from Still Images","date":"2022-03-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cmx-cross-modal-fusion-for-rgb-x-semantic","title":"CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers","date":"2022-03-09","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/exploring-event-driven-dynamic-context-for","title":"Exploring Event-driven Dynamic Context for Accident Scene Segmentation","date":"2021-12-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mefnet-multi-scale-event-fusion-network-for","title":"Event-Based Fusion for Motion Deblurring with Cross-modal Attention","date":"2021-11-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/specificity-preserving-rgb-d-saliency","title":"Specificity-preserving RGB-D Saliency Detection","date":"2021-08-18","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":14,"samples_ran":8,"samples_unverified":6,"pointer_only_for_licence":14,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/calibrated-rgb-d-salient-object-detection","title":"Calibrated RGB-D Salient Object Detection","date":"2021-06-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/segformer-simple-and-efficient-design-for","title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers","date":"2021-05-31","rows_on_this_dataset":1,"code_links":28,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":86,"samples_ran":64,"samples_unverified":22,"pointer_only_for_licence":15,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/combining-events-and-frames-using-recurrent","title":"Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Prediction","date":"2021-02-18","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/bi-directional-cross-modality-feature","title":"Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation","date":"2020-07-17","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/eca-net-efficient-channel-attention-for-deep","title":"ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks","date":"2019-10-08","rows_on_this_dataset":1,"code_links":13,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":8,"samples_ran":1,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/ev-segnet-semantic-segmentation-for-event","title":"EV-SegNet: Semantic Segmentation for Event-based Cameras","date":"2018-11-29","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/cbam-convolutional-block-attention-module","title":"CBAM: Convolutional Block Attention Module","date":"2018-07-17","rows_on_this_dataset":1,"code_links":31,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":22,"samples_ran":7,"samples_unverified":15,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/squeeze-and-excitation-networks","title":"Squeeze-and-Excitation Networks","date":"2017-09-05","rows_on_this_dataset":1,"code_links":85,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":9,"samples_ran":2,"samples_unverified":7,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":10,"samples_harvested":157,"samples_ran":98,"samples_unverified":59,"pointer_only_for_licence":39,"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."}