{"url":"/dataset/paris-lille-3d","name":"Paris-Lille-3D","full_name":null,"description_markdown":"The **Paris-Lille-3D** is a Benchmark on Point Cloud Classification. The Point Cloud has been labeled entirely by hand with 50 different classes. The dataset consists of around 2km of Mobile Laser System point cloud acquired in two cities in France (Paris and Lille).\r\n\r\nSource: [Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification](https://arxiv.org/pdf/1712.00032v2.pdf)\r\nImage Source: [https://npm3d.fr/paris-lille-3d](https://npm3d.fr/paris-lille-3d)","description_withheld":null,"homepage":"https://npm3d.fr/paris-lille-3d","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/paris-lille-3d-a-large-and-high-quality","title":"Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification","first_author":"Xavier Roynard","url":null},"license":{"name":"CC BY-NC-ND 3.0 FR","url":"https://creativecommons.org/licenses/by-nc-nd/3.0/fr/deed.en"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Few-Shot Learning","url":"/task/few-shot-learning","datasets_with_task":"/datasets/task/few-shot-learning"},{"name":"3D Semantic Segmentation","url":"/task/3d-semantic-segmentation","datasets_with_task":"/datasets/task/3d-semantic-segmentation"},{"name":"LIDAR Semantic Segmentation","url":"/task/lidar-semantic-segmentation","datasets_with_task":"/datasets/task/lidar-semantic-segmentation"}],"languages":[],"variants":["Paris-Lille-3D"],"data_loaders":[],"num_papers_in_archive":15,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/lidar-semantic-segmentation-on-paris-lille-3d","task":"LIDAR Semantic Segmentation","dataset_variant":"Paris-Lille-3D","rows":9,"metrics":["mIOU"],"first_row_in_archive_order":{"model":"FKAConv","paper":"/paper/lightconvpoint-convolution-for-points","metrics":{"mIOU":"0.827"},"code_links":[{"title":"valeoai/FKAConv","url":"https://github.com/valeoai/FKAConv"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/cloudspam-contrastive-learning-on-unlabeled","title":"CLOUDSPAM: Contrastive Learning On Unlabeled Data for Segmentation and Pre-Training Using Aggregated Point Clouds and MoCo","date":"2024-10-26","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/exploiting-local-geometry-for-feature-and","title":"Exploiting Local Geometry for Feature and Graph Construction for Better 3D Point Cloud Processing with Graph Neural Networks","date":"2021-03-28","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/fg-net-fast-large-scale-lidar-point","title":"FG-Net: Fast Large-Scale LiDAR Point Clouds Understanding Network Leveraging Correlated Feature Mining and Geometric-Aware Modelling","date":"2020-12-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/lightconvpoint-convolution-for-points","title":"FKAConv: Feature-Kernel Alignment for Point Cloud Convolution","date":"2020-04-09","rows_on_this_dataset":1,"code_links":1,"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/generalizing-discrete-convolutions-for","title":"ConvPoint: Continuous Convolutions for Point Cloud Processing","date":"2019-04-04","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":4,"samples_unverified":0,"pointer_only_for_licence":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/paris-lille-3d-a-large-and-high-quality","title":"Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification","date":"2017-11-30","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":16,"samples_ran":9,"samples_unverified":7,"pointer_only_for_licence":7,"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."}