{"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/adversarial-learning-for-unguided-single","title":"Adversarial learning for unguided single depth map completion of indoor scenes","arxiv_id":null,"date":"2025-01-07","proceeding":"Machine Vision and Applications 2025 1","authors":["Moushumi Medhi","Rajiv Ranjan Sahay"],"abstract":"Depth map completion without guidance from color images is a challenging, ill-posed problem. Conventional methods rely on computationally intensive optimization processes. This work proposes a deep adversarial learning approach to estimate missing depth information directly from a single degraded observation, without requiring RGB guidance or postprocessing.","url_abs":"https://link.springer.com/article/10.1007/s00138-024-01652-x","url_pdf":"https://link.springer.com/article/10.1007/s00138-024-01652-x","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":"adversarial-learning-for-unguided-single","repo_url":"https://github.com/Moushumi9medhi/Depth-Completion","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}