{"url":"/dataset/stpls3d","name":"STPLS3D","full_name":null,"description_markdown":"Our project (STPLS3D) aims to provide a large-scale aerial photogrammetry dataset with synthetic and real annotated 3D point clouds for semantic and instance segmentation tasks.\r\n\r\nAlthough various 3D datasets with different functions and scales have been proposed recently, it remains challenging for individuals to complete the whole pipeline of large-scale data collection, sanitization, and annotation (e.g., semantic and instance labels). Moreover, the created datasets usually suffer from extremely imbalanced class distribution or partial low-quality data samples. Motivated by this, we explore the procedurally synthetic 3D data generation paradigm to equip individuals with the full capability of creating large-scale annotated photogrammetry point clouds. Specifically, we introduce a synthetic aerial photogrammetry point clouds generation pipeline that takes full advantage of open geospatial data sources and off-the-shelf commercial packages. Unlike generating synthetic data in virtual games, where the simulated data usually have limited gaming environments created by artists, the proposed pipeline simulates the reconstruction process of the real environment by following the same UAV flight pattern on a wide variety of synthetic terrain shapes and building densities, which ensure similar quality, noise pattern, and diversity with real data. In addition, the precise semantic and instance annotations can be generated fully automatically, avoiding the expensive and time-consuming manual annotation process.  Based on the proposed pipeline, we present a richly-annotated synthetic 3D aerial photogrammetry point cloud dataset, termed STPLS3D, with more than 16 km^2 of landscapes and up to 18 fine-grained semantic categories. For verification purposes, we also provide a parallel dataset collected from four areas in the real environment.\r\n\r\nSource: [https://github.com/meidachen/STPLS3D](https://github.com/meidachen/STPLS3D)\r\nImage source: [https://github.com/meidachen/STPLS3D/blob/main/imgs/STPLS3D.png](https://github.com/meidachen/STPLS3D/blob/main/imgs/STPLS3D.png)","description_withheld":null,"homepage":"https://www.stpls3d.com/","introduced_date":"2022-03-17","introduced_date_note":null,"introduced_by":{"paper":"/paper/stpls3d-a-large-scale-synthetic-and-real","title":"STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset","first_author":"Meida Chen","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Instance Segmentation","url":"/task/instance-segmentation","datasets_with_task":"/datasets/task/instance-segmentation"},{"name":"3D Semantic Segmentation","url":"/task/3d-semantic-segmentation","datasets_with_task":"/datasets/task/3d-semantic-segmentation"},{"name":"3D Instance Segmentation","url":"/task/3d-instance-segmentation-1","datasets_with_task":"/datasets/task/3d-instance-segmentation-1"},{"name":"3D Open-Vocabulary Instance Segmentation","url":"/task/3d-open-vocabulary-instance-segmentation","datasets_with_task":"/datasets/task/3d-open-vocabulary-instance-segmentation"}],"languages":[],"variants":["STPLS3D"],"data_loaders":[{"repo":"https://github.com/meidachen/STPLS3D","url":"https://github.com/meidachen/STPLS3D","frameworks":["tf","pytorch"]}],"num_papers_in_archive":36,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-instance-segmentation-on-stpls3d","task":"3D Instance Segmentation","dataset_variant":"STPLS3D","rows":9,"metrics":["AP","AP50","AP25"],"first_row_in_archive_order":{"model":"EASE","paper":"/paper/edge-aware-3d-instance-segmentation-network","metrics":{"AP":"64.5","AP25":"86.9","AP50":"80.8"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/3d-semantic-segmentation-on-stpls3d","task":"3D Semantic Segmentation","dataset_variant":"STPLS3D","rows":6,"metrics":["mIOU"],"first_row_in_archive_order":{"model":"KpConv","paper":"/paper/kpconv-flexible-and-deformable-convolution","metrics":{"mIOU":"53.73"},"code_links":[{"title":"isl-org/Open3D-ML","url":"https://github.com/isl-org/Open3D-ML"},{"title":"HuguesTHOMAS/KPConv-PyTorch","url":"https://github.com/HuguesTHOMAS/KPConv-PyTorch"},{"title":"HuguesTHOMAS/KPConv","url":"https://github.com/HuguesTHOMAS/KPConv"},{"title":"ldkong1205/Robo3D","url":"https://github.com/ldkong1205/Robo3D"},{"title":"XuyangBai/KPConv.pytorch","url":"https://github.com/XuyangBai/KPConv.pytorch"},{"title":"plusmultiply/mprm","url":"https://github.com/plusmultiply/mprm"},{"title":"Arjun-NA/KPConv_for_DALES","url":"https://github.com/Arjun-NA/KPConv_for_DALES"},{"title":"Yacovitch/EyeNet","url":"https://github.com/Yacovitch/EyeNet"},{"title":"genglinliu/KPConv_Pytorch","url":"https://github.com/genglinliu/KPConv_Pytorch"},{"title":"JohnRomanelis/KPConv_torch_geometric","url":"https://github.com/JohnRomanelis/KPConv_torch_geometric"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/3d-open-vocabulary-instance-segmentation-on-3","task":"3D Open-Vocabulary Instance Segmentation","dataset_variant":"STPLS3D","rows":3,"metrics":["AP50"],"first_row_in_archive_order":{"model":"OPENINS3D","paper":"/paper/openins3d-snap-and-lookup-for-3d-open","metrics":{"AP50":"13.3"},"code_links":[{"title":"Pointcept/OpenIns3D","url":"https://github.com/Pointcept/OpenIns3D"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/edge-aware-3d-instance-segmentation-network","title":"Edge-Aware 3D Instance Segmentation Network with Intelligent Semantic Prior","date":"2024-01-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/pointct-point-central-transformer-network-for","title":"PointCT: Point Central Transformer Network for Weakly-supervised Point Cloud Semantic Segmentation","date":"2023-12-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/spherical-mask-coarse-to-fine-3d-point-cloud","title":"Spherical Mask: Coarse-to-Fine 3D Point Cloud Instance Segmentation with Spherical Representation","date":"2023-12-18","rows_on_this_dataset":1,"code_links":1,"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/openins3d-snap-and-lookup-for-3d-open","title":"OpenIns3D: Snap and Lookup for 3D Open-vocabulary Instance Segmentation","date":"2023-09-01","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/isbnet-a-3d-point-cloud-instance-segmentation","title":"ISBNet: a 3D Point Cloud Instance Segmentation Network with Instance-aware Sampling and Box-aware Dynamic Convolution","date":"2023-03-01","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":19,"samples_ran":2,"samples_unverified":17,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/top-down-beats-bottom-up-in-3d-instance","title":"Top-Down Beats Bottom-Up in 3D Instance Segmentation","date":"2023-02-06","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/pointclip-v2-adapting-clip-for-powerful-3d","title":"PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning","date":"2022-11-21","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":5,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mask3d-for-3d-semantic-instance-segmentation","title":"Mask3D: Mask Transformer for 3D Semantic Instance Segmentation","date":"2022-10-06","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":2,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/softgroup-scalable-3d-instance-segmentation","title":"Scalable SoftGroup for 3D Instance Segmentation on Point Clouds","date":"2022-09-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/softgroup-for-3d-instance-segmentation-on","title":"SoftGroup for 3D Instance Segmentation on Point Clouds","date":"2022-03-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pointclip-point-cloud-understanding-by-clip","title":"PointCLIP: Point Cloud Understanding by CLIP","date":"2021-12-04","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/hierarchical-aggregation-for-3d-instance","title":"Hierarchical Aggregation for 3D Instance Segmentation","date":"2021-08-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/scf-net-learning-spatial-contextual-features","title":"SCF-Net: Learning Spatial Contextual Features for Large-Scale Point Cloud Segmentation","date":"2021-06-19","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/point-transformer-1","title":"Point Transformer","date":"2020-12-16","rows_on_this_dataset":1,"code_links":24,"syntology":null},{"paper":"/paper/pointgroup-dual-set-point-grouping-for-3d","title":"PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation","date":"2020-04-03","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":0,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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":1,"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/pointnet-deep-hierarchical-feature-learning","title":"PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space","date":"2017-06-07","rows_on_this_dataset":1,"code_links":68,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":67,"samples_ran":36,"samples_unverified":31,"pointer_only_for_licence":26,"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":9,"samples_harvested":134,"samples_ran":53,"samples_unverified":81,"pointer_only_for_licence":31,"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."}