{"url":"/dataset/synlidar","name":"SynLiDAR","full_name":null,"description_markdown":"SynLiDAR is a large-scale synthetic LiDAR sequential point cloud dataset with point-wise annotations. 13 sequences of LiDAR point cloud with around 20k scans (over 19 billion points and 32 semantic classes) are collected from virtual urban cities, suburban towns, neighborhood, and harbor.","description_withheld":null,"homepage":"https://github.com/xiaoaoran/SynLiDAR","introduced_date":"2021-07-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/synlidar-learning-from-synthetic-lidar","title":"Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation","first_author":"Aoran Xiao","url":null},"license":null,"modalities":[{"name":"3D","url":"/datasets/modality/3d"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"},{"name":"LiDAR","url":"/datasets/modality/lidar"}],"tasks":[{"name":"Unsupervised Domain Adaptation","url":"/task/unsupervised-domain-adaptation","datasets_with_task":"/datasets/task/unsupervised-domain-adaptation"},{"name":"3D Semantic Segmentation","url":"/task/3d-semantic-segmentation","datasets_with_task":"/datasets/task/3d-semantic-segmentation"},{"name":"3D Source-Free Domain Adaptation","url":"/task/3d-source-free-domain-adaptation","datasets_with_task":"/datasets/task/3d-source-free-domain-adaptation"},{"name":"3D Unsupervised Domain Adaptation","url":"/task/3d-unsupervised-domain-adaptation","datasets_with_task":"/datasets/task/3d-unsupervised-domain-adaptation"}],"languages":[],"variants":["SynLiDAR","SynLiDAR-to-SemanticKITTI"],"data_loaders":[],"num_papers_in_archive":20,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-source-free-domain-adaptation-on-synlidar-1","task":"3D Source-Free Domain Adaptation","dataset_variant":"SynLiDAR-to-SemanticKITTI","rows":1,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"TTYD","paper":"/paper/train-till-you-drop-towards-stable-and-robust","metrics":{"mIoU":"32.4"},"code_links":[{"title":"valeoai/ttyd","url":"https://github.com/valeoai/ttyd"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/train-till-you-drop-towards-stable-and-robust","title":"Train Till You Drop: Towards Stable and Robust Source-free Unsupervised 3D Domain Adaptation","date":"2024-09-06","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":19,"samples_ran":10,"samples_unverified":9,"pointer_only_for_licence":19,"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":1,"samples_harvested":19,"samples_ran":10,"samples_unverified":9,"pointer_only_for_licence":19,"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."}