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DTSTAMP:20260817T171532Z
LOCATION:Hall B5 (2)\, B Block\, Level 5
DTSTART;TZID=Asia/Tokyo:20241203T130000
DTEND;TZID=Asia/Tokyo:20241203T131400
UID:siggraphasia_SIGGRAPH Asia 2024_sess104_papers_722@linklings.com
SUMMARY:InfNeRF: Towards Infinite Scale NeRF Rendering with O(log n) Space
  Complexity
DESCRIPTION:Jiabin Liang and Lanqing Zhang (Sea AI Lab), Zhuoran Zhao (Nat
 ional University of Singapore), and Xiangyu Xu (Xi'an Jiaotong University)
 \n\nThe conventional mesh-based Level of Detail (LoD) technique, exemplifi
 ed by applications such as Google Earth and many game engines, exhibits th
 e capability to holistically represent a large scene even the Earth, and a
 chieves rendering with a space complexity of O(log n).\nThis constrained d
 ata requirement not only enhances rendering efficiency but also facilitate
 s dynamic data fetching, thereby enabling a seamless 3D navigation experie
 nce for users.\nIn this work, we extend this proven LoD technique to Neura
 l Radiance Fields (NeRF) by introducing an octree structure to represent t
 he scenes in different scales. \nThis innovative approach provides a mathe
 matically simple and elegant representation with a rendering space complex
 ity of O(log n), aligned with the efficiency of mesh-based LoD techniques.
 \nWe also present a novel training strategy that maintains a complexity of
  O(n). \nThis strategy allows for parallel training with minimal overhead,
  ensuring the scalability and efficiency of our proposed method. \nOur con
 tribution is not only in extending the capabilities of existing techniques
  but also in establishing a foundation for scalable and efficient large-sc
 ale scene representation using NeRF and octree structures.\n\nRegistration
  Category: Full Access, Full Access Supporter\n\nLanguage Format: English 
 Language\n\nSession Chair: Bernhard Kerbl (Technical University of Vienna)
 \n\n
URL:https://asia.siggraph.org/2024/program/?id=papers_722&sess=sess104
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