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TZID:Asia/Tokyo
X-LIC-LOCATION:Asia/Tokyo
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DTSTART:18871231T000000
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DTSTAMP:20260817T171532Z
LOCATION:Hall B5 (2)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241204T105600
DTEND;TZID=Asia/Tokyo:20241204T110800
UID:siggraphasia_SIGGRAPH Asia 2024_sess113_papers_500@linklings.com
SUMMARY:Pano2Room: Novel View Synthesis from a Single Indoor Panorama
DESCRIPTION:Guo Pu, Yiming Zhao, and Zhouhui Lian (Wangxuan Institute of C
 omputer Technology, Peking University)\n\nRecent single-view 3D AIGC metho
 ds have made significant advancements by leveraging knowledge distilled fr
 om extensive 3D object datasets. However, challenges persist in the synthe
 sis of 3D scenes from a single view, primarily due to the complexity of re
 al-world environments and the limited availability of high-quality prior r
 esources.\nIn this paper, we introduce a novel approach called Pano2room, 
 designed to automatically reconstruct high-quality 3D indoor scenes from a
  single panoramic image. These panoramic images can be easily generated us
 ing a panoramic RGBD inpainter from captures at a single location with any
  camera.\nThe key idea is to initially construct a preliminary mesh from t
 he input panorama, and iteratively refine this mesh using a panoramic RGBD
  inpainter while collecting photo-realistic 3D-consistent pseudo novel vie
 ws. Finally the refined mesh is converted into a 3D Gaussian Splatting fie
 ld and trained with the collected pseudo novel views. This pipeline enable
 s the reconstruction of real-world 3D scenes, even in the presence of larg
 e occlusions, and facilitates the synthesis of photo-realistic novel views
  with detailed geometry.\nExtensive qualitative and quantitative experimen
 ts have been conducted to validate the superiority of our method in single
 -panorama indoor novel synthesis compared to the state of the art. Our cod
 e and data will be available at https://github.com/***.\n\nRegistration Ca
 tegory: Full Access, Full Access Supporter\n\nLanguage Format: English Lan
 guage\n\nSession Chair: Forrester Cole (Google)\n\n
URL:https://asia.siggraph.org/2024/program/?id=papers_500&sess=sess113
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