{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/arch2s-dataset-benchmark-and-challenges-for","title":"ARCH2S: Dataset, Benchmark and Challenges for Learning Exterior Architectural Structures from Point Clouds","arxiv_id":"2406.01337","date":"2024-06-03","proceeding":null,"authors":["Ka Lung Cheung","Chi Chung Lee"],"abstract":"Precise segmentation of architectural structures provides detailed information about various building components, enhancing our understanding and interaction with our built environment. Nevertheless, existing outdoor 3D point cloud datasets have limited and detailed annotations on architectural exteriors due to privacy concerns and the expensive costs of data acquisition and annotation. To overcome this shortfall, this paper introduces a semantically-enriched, photo-realistic 3D architectural models dataset and benchmark for semantic segmentation. It features 4 different building purposes of real-world buildings as well as an open architectural landscape in Hong Kong. Each point cloud is annotated into one of 14 semantic classes.","url_abs":"https://arxiv.org/abs/2406.01337v1","url_pdf":"https://arxiv.org/pdf/2406.01337v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"arch2s-dataset-benchmark-and-challenges-for","repo_url":"https://github.com/Semanticity-Research/ARCH2S","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-scene-reconstruction","task_name":"3D Scene Reconstruction"},{"task_slug":"3d-semantic-segmentation","task_name":"3D Semantic Segmentation"},{"task_slug":"point-cloud-generation","task_name":"Point Cloud Generation"},{"task_slug":"point-cloud-segmentation","task_name":"Point Cloud Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"3d-cnn","method_name":"3D CNN"},{"method_slug":"3d-convolution","method_name":"3D Convolution"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[{"slug":"arch2s","name":"ARCH2S","full_name":"Dataset, Benchmark for Learning Exterior Architectural Structures from Point Clouds"}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}