{"url":"/dataset/arch2s","name":"ARCH2S","full_name":"Dataset, Benchmark for Learning Exterior Architectural Structures from Point Clouds","description_markdown":"Precise segmentation of architectural structures\r\nprovides detailed information about various building\r\ncomponents, enhancing our understanding and\r\ninteraction with our built environment. Nevertheless,\r\nexisting outdoor 3D point cloud datasets have\r\nlimited and detailed annotations on architectural\r\nexteriors due to privacy concerns and the expensive\r\ncosts of data acquisition and annotation. To\r\novercome this shortfall, this paper introduces a\r\nsemantically-enriched, photo-realistic 3D architectural\r\nmodels dataset and benchmark for semantic\r\nsegmentation. It features 4 different building purposes\r\nof real-world buildings as well as an open\r\narchitectural landscape in Hong Kong. Each point\r\ncloud is annotated into one of 14 semantic classes.","description_withheld":null,"homepage":"","introduced_date":"2024-06-03","introduced_date_note":null,"introduced_by":{"paper":"/paper/arch2s-dataset-benchmark-and-challenges-for","title":"ARCH2S: Dataset, Benchmark and Challenges for Learning Exterior Architectural Structures from Point Clouds","first_author":"Ka Lung Cheung","url":null},"license":{"name":"MIT","url":"https://github.com/Semanticity-Research/ARCH2S?tab=MIT-1-ov-file"},"modalities":[{"name":"3D","url":"/datasets/modality/3d"},{"name":"Point cloud","url":"/datasets/modality/point-cloud"},{"name":"Environment","url":"/datasets/modality/environment"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"3D Semantic Segmentation","url":"/task/3d-semantic-segmentation","datasets_with_task":"/datasets/task/3d-semantic-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["ARCH2S"],"data_loaders":[{"repo":"https://github.com/Semanticity-Research/ARCH2S","url":"https://github.com/Semanticity-Research/ARCH2S","frameworks":["pytorch"]}],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/semantic-segmentation-on-arch2s","task":"Semantic Segmentation","dataset_variant":"ARCH2S","rows":1,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"BIM-Net","paper":"/paper/fully-automated-scan-to-bim-via-point-cloud","metrics":{"mIoU":"18.4"},"code_links":[{"title":"LTTM/Scan-to-BIM","url":"https://github.com/LTTM/Scan-to-BIM"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/fully-automated-scan-to-bim-via-point-cloud","title":"Fully Automated Scan-to-BIM Via Point Cloud Instance Segmentation","date":"2023-09-11","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}