{"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/h2o-sdf-two-phase-learning-for-3d-indoor","title":"H2O-SDF: Two-phase Learning for 3D Indoor Reconstruction using Object Surface Fields","arxiv_id":"2402.08138","date":"2024-02-13","proceeding":null,"authors":["Minyoung Park","Mirae Do","YeonJae Shin","Jaeseok Yoo","Jongkwang Hong","Joongrock Kim","Chul Lee"],"abstract":"Advanced techniques using Neural Radiance Fields (NeRF), Signed Distance Fields (SDF), and Occupancy Fields have recently emerged as solutions for 3D indoor scene reconstruction. We introduce a novel two-phase learning approach, H2O-SDF, that discriminates between object and non-object regions within indoor environments. This method achieves a nuanced balance, carefully preserving the geometric integrity of room layouts while also capturing intricate surface details of specific objects. A cornerstone of our two-phase learning framework is the introduction of the Object Surface Field (OSF), a novel concept designed to mitigate the persistent vanishing gradient problem that has previously hindered the capture of high-frequency details in other methods. Our proposed approach is validated through several experiments that include ablation studies.","url_abs":"https://arxiv.org/abs/2402.08138v2","url_pdf":"https://arxiv.org/pdf/2402.08138v2.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":"h2o-sdf-two-phase-learning-for-3d-indoor","repo_url":"https://github.com/Domirae/H2O-SDF","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"indoor-scene-reconstruction","task_name":"Indoor Scene Reconstruction"},{"task_slug":"nerf","task_name":"NeRF"},{"task_slug":"object","task_name":"Object"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2402.08138","atlas_url":"https://app.syntology.ai/?focus=2402.08138","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}