{"url":"/dataset/pointcloud-c","name":"PointCloud-C","full_name":null,"description_markdown":"PointCloud-C is the very first test-suite for point cloud robustness analysis under corruptions.\r\n\r\n- Two sets: ModelNet-C for point cloud classification and ShapeNet-C for part segmentation.\r\n- Real-world corruption sources, ranging from object-, senor-, and processing-levels.\r\n- Seven types of corruptions, each with five severity levels.\r\n- Benchmark with more than 20 point cloud recognition algorithms.\r\n- Methods ranging from architecture design, augmentations, and pre-training.","description_withheld":null,"homepage":"https://pointcloud-c.github.io/home.html","introduced_date":"2022-06-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/benchmarking-and-analyzing-point-cloud","title":"Benchmarking and Analyzing Point Cloud Classification under Corruptions","first_author":"Jiawei Ren","url":null},"license":{"name":"Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License","url":"http://creativecommons.org/licenses/by-nc-sa/4.0/"},"modalities":[{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[{"name":"Point Cloud Classification","url":"/task/point-cloud-classification","datasets_with_task":"/datasets/task/point-cloud-classification"},{"name":"Point Cloud Segmentation","url":"/task/point-cloud-segmentation","datasets_with_task":"/datasets/task/point-cloud-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["PointCloud-C"],"data_loaders":[{"repo":"https://github.com/ldkong1205/PointCloud-C","url":"https://github.com/ldkong1205/PointCloud-C","frameworks":["pytorch"]}],"num_papers_in_archive":23,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/point-cloud-classification-on-pointcloud-c","task":"Point Cloud Classification","dataset_variant":"PointCloud-C","rows":24,"metrics":["mean Corruption Error (mCE)"],"first_row_in_archive_order":{"model":"BeyondRPC","paper":"/paper/beyondrpc-a-contrastive-and-augmentation","metrics":{"mean Corruption Error (mCE)":"0.455"},"code_links":[{"title":"virgantara/BeyondRPC","url":"https://github.com/virgantara/BeyondRPC"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/point-cloud-segmentation-on-pointcloud-c","task":"Point Cloud Segmentation","dataset_variant":"PointCloud-C","rows":11,"metrics":["mean Corruption Error (mCE)"],"first_row_in_archive_order":{"model":"GDANet","paper":"/paper/learning-geometry-disentangled-representation","metrics":{"mean Corruption Error (mCE)":"0.923"},"code_links":[{"title":"mutianxu/GDANet","url":"https://github.com/mutianxu/GDANet"},{"title":"yossilevii100/refocusing","url":"https://github.com/yossilevii100/refocusing"},{"title":"yossilevii100/critical_points2","url":"https://github.com/yossilevii100/critical_points2"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/beyondrpc-a-contrastive-and-augmentation","title":"BeyondRPC: A Contrastive and Augmentation-Driven Framework for Robust Point Cloud Understanding","date":"2025-06-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/critical-points-an-agile-point-cloud","title":"Robustifying Point Cloud Networks by Refocusing","date":"2023-08-10","rows_on_this_dataset":3,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":22,"samples_ran":15,"samples_unverified":7,"pointer_only_for_licence":22,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/epic-ensemble-of-partial-point-clouds-for","title":"EPiC: Ensemble of Partial Point Clouds for Robust Classification","date":"2023-03-20","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/masked-autoencoders-for-point-cloud-self","title":"Masked Autoencoders for Point Cloud Self-supervised Learning","date":"2022-03-13","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":7,"samples_unverified":2,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/rethinking-network-design-and-local-geometry-1","title":"Rethinking Network Design and Local Geometry in Point Cloud: A Simple Residual MLP Framework","date":"2022-02-15","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":10,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/benchmarking-and-analyzing-point-cloud","title":"Benchmarking and Analyzing Point Cloud Classification under Corruptions","date":"2022-02-07","rows_on_this_dataset":5,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":6,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/point-cloud-augmentation-with-weighted-local-1","title":"Point Cloud Augmentation with Weighted Local Transformations","date":"2021-10-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/revisiting-point-cloud-shape-classification","title":"Revisiting Point Cloud Shape Classification with a Simple and Effective Baseline","date":"2021-06-09","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":4,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; 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not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":14,"samples_harvested":358,"samples_ran":196,"samples_unverified":162,"pointer_only_for_licence":173,"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."}