{"url":"/dataset/semantickitti-c","name":"SemanticKITTI-C","full_name":null,"description_markdown":"#### 🤖 Robo3D - The SemanticKITTI-C Benchmark\r\n\r\nSemanticKITTI-C is an evaluation benchmark heading toward robust and reliable 3D semantic segmentation in autonomous driving. With it, we probe the robustness of 3D 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\nSemanticKITTI-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 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":["SemanticKITTI-C"],"data_loaders":[{"repo":"https://github.com/ldkong1205/Robo3D","url":"https://github.com/ldkong1205/Robo3D","frameworks":["pytorch"]}],"num_papers_in_archive":26,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/robust-3d-semantic-segmentation-on","task":"Robust 3D Semantic Segmentation","dataset_variant":"SemanticKITTI-C","rows":22,"metrics":["mean Corruption Error (mCE)"],"first_row_in_archive_order":{"model":"SPVCNN-34","paper":"/paper/searching-efficient-3d-architectures-with","metrics":{"mean Corruption Error (mCE)":"99.16%"},"code_links":[{"title":"Pointcept/Pointcept","url":"https://github.com/Pointcept/Pointcept"},{"title":"mit-han-lab/torchsparse","url":"https://github.com/mit-han-lab/torchsparse"},{"title":"mit-han-lab/spvnas","url":"https://github.com/mit-han-lab/spvnas"},{"title":"pjlab-adg/openpcseg","url":"https://github.com/pjlab-adg/openpcseg"},{"title":"pjlab-adg/pcseg","url":"https://github.com/pjlab-adg/pcseg"},{"title":"chenfengxu714/image2point","url":"https://github.com/chenfengxu714/image2point"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/using-a-waffle-iron-for-automotive-point","title":"Using a Waffle Iron for Automotive Point Cloud Semantic Segmentation","date":"2023-01-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":5,"samples_unverified":0,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pids-joint-point-interaction-dimension-search","title":"PIDS: Joint Point Interaction-Dimension Search for 3D Point Cloud","date":"2022-11-28","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/cenet-toward-concise-and-efficient-lidar","title":"CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving","date":"2022-07-26","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/2dpass-2d-priors-assisted-semantic","title":"2DPASS: 2D Priors Assisted Semantic Segmentation on LiDAR Point Clouds","date":"2022-07-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/gfnet-geometric-flow-network-for-3d-point","title":"GFNet: Geometric Flow Network for 3D Point Cloud Semantic Segmentation","date":"2022-07-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cpgnet-cascade-point-grid-fusion-network-for","title":"CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation","date":"2022-04-21","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":1,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fidnet-lidar-point-cloud-semantic","title":"FIDNet: LiDAR Point Cloud Semantic Segmentation with Fully Interpolation Decoding","date":"2021-09-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/rpvnet-a-deep-and-efficient-range-point-voxel","title":"RPVNet: A Deep and Efficient Range-Point-Voxel Fusion Network for LiDAR Point Cloud Segmentation","date":"2021-03-24","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"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":2,"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/polarnet-an-improved-grid-representation-for","title":"PolarNet: An Improved Grid Representation for Online LiDAR Point Clouds Semantic Segmentation","date":"2020-03-31","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":9,"samples_unverified":2,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/salsanext-fast-semantic-segmentation-of-lidar","title":"SalsaNext: Fast, Uncertainty-aware Semantic Segmentation of LiDAR Point Clouds for Autonomous Driving","date":"2020-03-07","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":4,"samples_unverified":7,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/rangenet-fast-and-accurate-lidar-semantic","title":"RangeNet++: Fast and Accurate LiDAR Semantic Segmentation","date":"2019-11-04","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/kpconv-flexible-and-deformable-convolution","title":"KPConv: Flexible and Deformable Convolution for Point Clouds","date":"2019-04-18","rows_on_this_dataset":1,"code_links":10,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":5,"samples_unverified":7,"pointer_only_for_licence":3,"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."}},{"paper":"/paper/squeezesegv2-improved-model-structure-and","title":"SqueezeSegV2: Improved Model Structure and Unsupervised Domain Adaptation for Road-Object Segmentation from a LiDAR Point Cloud","date":"2018-09-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":0,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/squeezeseg-convolutional-neural-nets-with","title":"SqueezeSeg: Convolutional Neural Nets with Recurrent CRF for Real-Time Road-Object Segmentation from 3D LiDAR Point Cloud","date":"2017-10-19","rows_on_this_dataset":1,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":0,"samples_unverified":7,"pointer_only_for_licence":0,"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":10,"samples_harvested":60,"samples_ran":25,"samples_unverified":35,"pointer_only_for_licence":12,"papers_with_no_sample_that_ran":4,"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."}