{"url":"/sota/3d-object-detection-on-stf","task":{"name":"3D Object Detection","url":"/task/3d-object-detection","note":null},"dataset":{"name":"Dense Fog","url":"/dataset/stf"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"**3D Object Detection** is a task in computer vision where the goal is to identify and locate objects in a 3D environment based on their shape, location, and orientation. It involves detecting the presence of objects and determining their location in the 3D space in real-time. This task is crucial for applications such as autonomous vehicles, robotics, and augmented reality.\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [AVOD](https://github.com/kujason/avod) )</span>","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["mod. Car AP@.5IoU","mod. Cyclist AP@.25IoU","mod. Pedestrian AP@.25IoU","mod. mAP"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"mod. Car AP@.5IoU":"higher","mod. Cyclist AP@.25IoU":"higher","mod. Pedestrian AP@.25IoU":"higher","mod. mAP":"higher"}},"counts":{"rows":1,"rows_with_code":1,"rows_with_paper_page":1,"rows_dated":1,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"PV-RCNN","metrics":{"mod. Car AP@.5IoU":"47.38","mod. Cyclist AP@.25IoU":"27.89","mod. Pedestrian AP@.25IoU":"40.65","mod. mAP":"38.64"},"uses_additional_data":false,"paper_date":"2021-08-11","paper":"/paper/fog-simulation-on-real-lidar-point-clouds-for","paper_url":"https://arxiv.org/abs/2108.05249v3","paper_title":"Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather","code":"https://github.com/MartinHahner/LiDAR_fog_sim","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":5,"n_samples":6,"n_pointer_only_licence":6}}],"since_archive":{"present":false,"note":"No Syntology-extracted rows are published in this build."},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":1,"rows_with_any_sample_ran":1,"distinct_papers_with_graph_line":1,"distinct_papers_with_any_sample_ran":1,"samples_over_distinct_papers":{"n_ran":1,"n_unverified":5,"n_samples":6,"n_pointer_only_licence":6,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":1,"n_unverified":5,"n_samples":6,"n_pointer_only_licence":6,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}