{"url":"/dataset/simbev","name":"SimBEV","full_name":null,"description_markdown":"The **SimBEV** dataset is a collection of 320 scenes spread across all 11 CARLA maps and contains data from a variety of sensors, including five camera types (RGB, semantic segmentation, instance segmentation, depth, and optical flow), lidar, semantic lidar, radar, GNSS, and IMU, along with 3D object bounding boxes and accurate bird's-eye view (BEV) ground truth. With each scene lasting 16 seconds at a frame rate of 20 Hz, the SimBEV dataset contains 102,400 annotated frames, over 8 million 3D object bounding boxes, and more than 2.5 billion BEV ground truth labels.","description_withheld":null,"homepage":"https://drive.google.com/drive/folders/14MytQeGmW80Btg_AGPNrE18ZLdLzyGx5?usp=sharing","introduced_date":"2025-02-04","introduced_date_note":null,"introduced_by":{"paper":"/paper/simbev-a-synthetic-multi-task-multi-sensor","title":"SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and Dataset","first_author":"Goodarz Mehr","url":null},"license":{"name":"CC BY-NC-SA 4.0","url":"https://creativecommons.org/licenses/by-nc-sa/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"},{"name":"LiDAR","url":"/datasets/modality/lidar"}],"tasks":[{"name":"2D Object Detection","url":"/task/2d-object-detection","datasets_with_task":"/datasets/task/2d-object-detection"},{"name":"2D Semantic Segmentation","url":"/task/2d-semantic-segmentation","datasets_with_task":"/datasets/task/2d-semantic-segmentation"},{"name":"3D Object Detection","url":"/task/3d-object-detection","datasets_with_task":"/datasets/task/3d-object-detection"},{"name":"Depth Estimation","url":"/task/depth-estimation","datasets_with_task":"/datasets/task/depth-estimation"},{"name":"Bird's-Eye View Semantic Segmentation","url":"/task/bird-s-eye-view-semantic-segmentation","datasets_with_task":"/datasets/task/bird-s-eye-view-semantic-segmentation"},{"name":"3D Object Tracking","url":"/task/3d-object-tracking","datasets_with_task":"/datasets/task/3d-object-tracking"},{"name":"BEV Segmentation","url":"/task/bev-segmentation","datasets_with_task":"/datasets/task/bev-segmentation"},{"name":"3D Semantic Occupancy Prediction","url":"/task/3d-semantic-occupancy-prediction","datasets_with_task":"/datasets/task/3d-semantic-occupancy-prediction"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["SimBEV"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-object-detection-on-simbev","task":"3D Object Detection","dataset_variant":"SimBEV","rows":5,"metrics":["SDS","mAP","mATE","mAOE","mASE","mAVE"],"first_row_in_archive_order":{"model":"UniTR+LSS","paper":"/paper/simbev-a-synthetic-multi-task-multi-sensor","metrics":{"SDS":"0.622","mAOE":"0.207","mAP":"0.478","mASE":"0.085","mATE":"0.113","mAVE":"0.53"},"code_links":[{"title":"goodarzmehr/simbev","url":"https://github.com/goodarzmehr/simbev"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/bev-segmentation-on-simbev","task":"BEV Segmentation","dataset_variant":"SimBEV","rows":5,"metrics":["mIoU","road","car","truck","bus","motorcycle","bicycle","rider","pedestrian"],"first_row_in_archive_order":{"model":"BEVFusion","paper":"/paper/simbev-a-synthetic-multi-task-multi-sensor","metrics":{"bicycle":"0.036","bus":"0.8","car":"0.727","mIoU":"0.5","motorcycle":"0.363","pedestrian":"0.2","rider":"0.233","road":"0.884","truck":"0.745"},"code_links":[{"title":"goodarzmehr/simbev","url":"https://github.com/goodarzmehr/simbev"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/bird-s-eye-view-semantic-segmentation-on-1","task":"Bird's-Eye View Semantic Segmentation","dataset_variant":"SimBEV","rows":5,"metrics":["mIoU","road","car","truck","bus","motorcycle","bicycle","rider","pedestrian"],"first_row_in_archive_order":{"model":"BEVFusion","paper":"/paper/simbev-a-synthetic-multi-task-multi-sensor","metrics":{"bicycle":"0.036","bus":"0.808","car":"0.727","mIoU":"0.5","motorcycle":"0.363","pedestrian":"0.202","rider":"0.233","road":"0.884","truck":"0.745"},"code_links":[{"title":"goodarzmehr/simbev","url":"https://github.com/goodarzmehr/simbev"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/simbev-a-synthetic-multi-task-multi-sensor","title":"SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and Dataset","date":"2025-02-04","rows_on_this_dataset":15,"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."}