{"url":"/dataset/sydney-urban-objects","name":"Sydney Urban Objects","full_name":null,"description_markdown":"This dataset contains a variety of common urban road objects scanned with a Velodyne HDL-64E LIDAR, collected in the CBD of Sydney, Australia. There are 631 individual scans of objects across classes of vehicles, pedestrians, signs and trees.\n\nIt was collected in order to test matching and classification algorithms. It aims to provide non-ideal sensing conditions that are representative of practical urban sensing systems, with a large variability in viewpoint and occlusion.\n\nSource: [http://www.acfr.usyd.edu.au/papers/SydneyUrbanObjectsDataset.shtml](http://www.acfr.usyd.edu.au/papers/SydneyUrbanObjectsDataset.shtml)\nImage Source: [http://www.acfr.usyd.edu.au/papers/SydneyUrbanObjectsDataset.shtml](http://www.acfr.usyd.edu.au/papers/SydneyUrbanObjectsDataset.shtml)","description_withheld":null,"homepage":"http://www.acfr.usyd.edu.au/papers/SydneyUrbanObjectsDataset.shtml","introduced_date":null,"introduced_date_note":null,"introduced_by":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":"3D Point Cloud Classification","url":"/task/3d-point-cloud-classification","datasets_with_task":"/datasets/task/3d-point-cloud-classification"}],"languages":[],"variants":["Sydney Urban Objects"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-point-cloud-classification-on-sydney-urban","task":"3D Point Cloud Classification","dataset_variant":"Sydney Urban Objects","rows":3,"metrics":["F1"],"first_row_in_archive_order":{"model":"ECC","paper":"/paper/dynamic-edge-conditioned-filters-in","metrics":{"F1":"78.4"},"code_links":[{"title":"rusty1s/pytorch_cluster","url":"https://github.com/rusty1s/pytorch_cluster"},{"title":"mys007/ecc","url":"https://github.com/mys007/ecc"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/general-purpose-deep-point-cloud-feature","title":"General-Purpose Deep Point Cloud Feature Extractor","date":"2018-03-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dynamic-edge-conditioned-filters-in","title":"Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphs","date":"2017-04-10","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/orientation-boosted-voxel-nets-for-3d-object","title":"Orientation-boosted Voxel Nets for 3D Object Recognition","date":"2016-04-12","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"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."}