{"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/srda-generating-instance-segmentation","title":"SRDA: Generating Instance Segmentation Annotation Via Scanning, Reasoning And Domain Adaptation","arxiv_id":"1801.08839","date":"2018-01-26","proceeding":"ECCV 2018 9","authors":["Wenqiang Xu","Yonglu Li","Cewu Lu"],"abstract":"Instance segmentation is a problem of significance in computer vision.\nHowever, preparing annotated data for this task is extremely time-consuming and\ncostly. By combining the advantages of 3D scanning, reasoning, and GAN-based\ndomain adaptation techniques, we introduce a novel pipeline named SRDA to\nobtain large quantities of training samples with very minor effort. Our\npipeline is well-suited to scenes that can be scanned, i.e. most indoor and\nsome outdoor scenarios. To evaluate our performance, we build three\nrepresentative scenes and a new dataset, with 3D models of various common\nobjects categories and annotated real-world scene images. Extensive experiments\nshow that our pipeline can achieve decent instance segmentation performance\ngiven very low human labor cost.","url_abs":"http://arxiv.org/abs/1801.08839v3","url_pdf":"http://arxiv.org/pdf/1801.08839v3.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":"srda-generating-instance-segmentation","repo_url":"https://github.com/DirtyHarryLYL/SRDA-ECCV2018","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}