{"url":"/dataset/wod-c","name":"WOD-C","full_name":null,"description_markdown":"#### 🤖 Robo3D - The WOD-C Benchmark\r\n\r\nWOD-C is an evaluation benchmark heading toward robust and reliable 3D perception in autonomous driving. With it, we probe the robustness of 3D detectors and segmentors 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\nWOD-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":"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":"3D Semantic Segmentation","url":"/task/3d-semantic-segmentation","datasets_with_task":"/datasets/task/3d-semantic-segmentation"},{"name":"Robust 3D Semantic Segmentation","url":"/task/robust-3d-semantic-segmentation","datasets_with_task":"/datasets/task/robust-3d-semantic-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["WOD-C"],"data_loaders":[{"repo":"https://github.com/ldkong1205/Robo3D","url":"https://github.com/ldkong1205/Robo3D","frameworks":["pytorch"]}],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/robust-3d-semantic-segmentation-on-wod-c","task":"Robust 3D Semantic Segmentation","dataset_variant":"WOD-C","rows":5,"metrics":["mean Corruption Error (mCE)"],"first_row_in_archive_order":{"model":"MinkUNet-34","paper":"/paper/4d-spatio-temporal-convnets-minkowski","metrics":{"mean Corruption Error (mCE)":"96.21%"},"code_links":[{"title":"NVIDIA/MinkowskiEngine","url":"https://github.com/NVIDIA/MinkowskiEngine"},{"title":"StanfordVL/MinkowskiEngine","url":"https://github.com/StanfordVL/MinkowskiEngine"},{"title":"Pointcept/Pointcept","url":"https://github.com/Pointcept/Pointcept"},{"title":"mit-han-lab/spvnas","url":"https://github.com/mit-han-lab/spvnas"},{"title":"ldkong1205/Robo3D","url":"https://github.com/ldkong1205/Robo3D"},{"title":"buildingnet/buildingnet_dataset","url":"https://github.com/buildingnet/buildingnet_dataset"},{"title":"shwoo93/minkowskiengine","url":"https://github.com/shwoo93/minkowskiengine"},{"title":"dkoh0207/lartpc_minkowski","url":"https://github.com/dkoh0207/lartpc_minkowski"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/cylindrical-and-asymmetrical-3d-convolution","title":"Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR Segmentation","date":"2020-11-19","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/searching-efficient-3d-architectures-with","title":"Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution","date":"2020-07-31","rows_on_this_dataset":2,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":0,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/4d-spatio-temporal-convnets-minkowski","title":"4D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networks","date":"2019-04-18","rows_on_this_dataset":2,"code_links":8,"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":2,"samples_harvested":5,"samples_ran":1,"samples_unverified":4,"pointer_only_for_licence":2,"papers_with_no_sample_that_ran":1,"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."}