{"url":"/dataset/kitti-c","name":"KITTI-C","full_name":null,"description_markdown":"#### 🤖 Robo3D - The KITTI-C Benchmark\r\n\r\nKITTI-C is an evaluation benchmark heading toward robust and reliable 3D object detection in autonomous driving. With it, we probe the robustness of 3D detectors under out-of-distribution (OoD) scenarios against corruptions that occur in the real-world environment. Specifically, we consider natural corruptions happen in the following cases:\r\n\r\n- Adverse weather conditions, such as fog, wet ground, and snow;\r\n- External disturbances that are caused by motion blur or result in LiDAR beam missing;\r\n- Internal sensor failure, including crosstalk, possible incomplete echo, and cross-sensor scenarios.\r\n\r\nKITTI-C is part of the [Robo3D](https://arxiv.org/abs/2303.17597) benchmark. Visit our homepage to explore more details.","description_withheld":null,"homepage":"https://ldkong.com/Robo3D","introduced_date":"2023-03-30","introduced_date_note":null,"introduced_by":{"paper":"/paper/robo3d-towards-robust-and-reliable-3d","title":"Robo3D: Towards Robust and Reliable 3D Perception against Corruptions","first_author":"Lingdong Kong","url":null},"license":{"name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"}],"tasks":[{"name":"3D Object Detection","url":"/task/3d-object-detection","datasets_with_task":"/datasets/task/3d-object-detection"},{"name":"Unsupervised Monocular Depth Estimation","url":"/task/unsupervised-monocular-depth-estimation","datasets_with_task":"/datasets/task/unsupervised-monocular-depth-estimation"},{"name":"Robust 3D Object Detection","url":"/task/robust-3d-object-detection","datasets_with_task":"/datasets/task/robust-3d-object-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["KITTI-C"],"data_loaders":[{"repo":"https://github.com/ldkong1205/Robo3D","url":"https://github.com/ldkong1205/Robo3D","frameworks":["pytorch"]}],"num_papers_in_archive":30,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/robust-3d-object-detection-on-kitti-c","task":"Robust 3D Object Detection","dataset_variant":"KITTI-C","rows":5,"metrics":["mean Corruption Error (mCE)"],"first_row_in_archive_order":{"model":"PV-RCNN","paper":"/paper/pv-rcnn-point-voxel-feature-set-abstraction","metrics":{"mean Corruption Error (mCE)":"90.04%"},"code_links":[{"title":"open-mmlab/OpenPCDet","url":"https://github.com/open-mmlab/OpenPCDet"},{"title":"jhultman/PV-RCNN","url":"https://github.com/jhultman/PV-RCNN"},{"title":"sshaoshuai/PV-RCNN","url":"https://github.com/sshaoshuai/PV-RCNN"},{"title":"KangchengLiu/FAC_Foreground_Aware_Contrast","url":"https://github.com/KangchengLiu/FAC_Foreground_Aware_Contrast"},{"title":"KangchengLiu/RM3D","url":"https://github.com/KangchengLiu/RM3D"},{"title":"KPeng9510/MASS","url":"https://github.com/KPeng9510/MASS"},{"title":"code-implementation1/Code7","url":"https://github.com/code-implementation1/Code7/tree/main/rcnn"},{"title":"sunshenggu/xc_eval_pcdet","url":"https://github.com/sunshenggu/xc_eval_pcdet"},{"title":"2023-MindSpore-1/ms-code-6","url":"https://github.com/2023-MindSpore-1/ms-code-6/tree/main/CascadeRCNN"},{"title":"MindSpore-paper-code-3/code6","url":"https://github.com/MindSpore-paper-code-3/code6/tree/main/CascadeRCNN"},{"title":"code-implementation1/Code5","url":"https://github.com/code-implementation1/Code5/tree/main/ms_rcnn"},{"title":"2023-MindSpore-1/ms-code-220","url":"https://github.com/2023-MindSpore-1/ms-code-220/tree/main/textrcnn"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/center-based-3d-object-detection-and-tracking","title":"Center-based 3D Object Detection and Tracking","date":"2020-06-19","rows_on_this_dataset":1,"code_links":13,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":22,"samples_ran":7,"samples_unverified":15,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pv-rcnn-point-voxel-feature-set-abstraction","title":"PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection","date":"2019-12-31","rows_on_this_dataset":1,"code_links":12,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":16,"samples_ran":2,"samples_unverified":14,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pointpillars-fast-encoders-for-object","title":"PointPillars: Fast Encoders for Object Detection from Point Clouds","date":"2018-12-14","rows_on_this_dataset":2,"code_links":18,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":2,"samples_unverified":13,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pointrcnn-3d-object-proposal-generation-and","title":"PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud","date":"2018-12-11","rows_on_this_dataset":1,"code_links":13,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":5,"samples_unverified":4,"pointer_only_for_licence":4,"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":4,"samples_harvested":62,"samples_ran":16,"samples_unverified":46,"pointer_only_for_licence":5,"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."}