{"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/semantic-stereo-for-incidental-satellite","title":"Semantic Stereo for Incidental Satellite Images","arxiv_id":"1811.08739","date":"2018-11-21","proceeding":null,"authors":["Marc Bosch","Kevin Foster","Gordon Christie","Sean Wang","Gregory D. Hager","Myron Brown"],"abstract":"The increasingly common use of incidental satellite images for stereo\nreconstruction versus rigidly tasked binocular or trinocular coincident\ncollection is helping to enable timely global-scale 3D mapping; however,\nreliable stereo correspondence from multi-date image pairs remains very\nchallenging due to seasonal appearance differences and scene change. Promising\nrecent work suggests that semantic scene segmentation can provide a robust\nregularizing prior for resolving ambiguities in stereo correspondence and\nreconstruction problems. To enable research for pairwise semantic stereo and\nmulti-view semantic 3D reconstruction with incidental satellite images, we have\nestablished a large-scale public dataset including multi-view, multi-band\nsatellite images and ground truth geometric and semantic labels for two large\ncities. To demonstrate the complementary nature of the stereo and segmentation\ntasks, we present lightweight public baselines adapted from recent state of the\nart convolutional neural network models and assess their performance.","url_abs":"http://arxiv.org/abs/1811.08739v1","url_pdf":"http://arxiv.org/pdf/1811.08739v1.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":"semantic-stereo-for-incidental-satellite","repo_url":"https://github.com/pubgeo/dfc2019","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"3d-reconstruction","task_name":"3D Reconstruction"},{"task_slug":"scene-segmentation","task_name":"Scene Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1811.08739","atlas_url":"https://app.syntology.ai/?focus=1811.08739","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}