{"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/insertnerf-instilling-generalizability-into","title":"InsertNeRF: Instilling Generalizability into NeRF with HyperNet Modules","arxiv_id":"2308.13897","date":"2023-08-26","proceeding":null,"authors":["Yanqi Bao","Tianyu Ding","Jing Huo","Wenbin Li","Yuxin Li","Yang Gao"],"abstract":"Generalizing Neural Radiance Fields (NeRF) to new scenes is a significant challenge that existing approaches struggle to address without extensive modifications to vanilla NeRF framework. We introduce InsertNeRF, a method for INStilling gEneRalizabiliTy into NeRF. 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