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BEGIN:VEVENT
DTSTAMP:20260114T163645Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231215T154000
DTEND;TZID=Australia/Melbourne:20231215T155000
UID:siggraphasia_SIGGRAPH Asia 2023_sess159_papers_565@linklings.com
SUMMARY:Anti-Aliased Neural Implicit Surfaces with Encoding Level of Detai
 l
DESCRIPTION:Yiyu Zhuang (Nanjing University); Qi Zhang and Ying Feng (Tenc
 ent); Hao Zhu and Yao Yao (Nanjing University); Xiaoyu Li, Yan-Pei Cao, an
 d Ying Shan (Tencent); and Xun Cao (Nanjing University)\n\nWe present LoD-
 NeuS, an efficient neural representation for high-frequency geometry detai
 l recovery and anti-aliased novel view rendering. Drawing inspiration from
  voxel-based representations with the level of detail (LoD), we introduce 
 a multi-scale tri-plane-based scene representation that is capable of capt
 uring the LoD of the signed distance function (SDF) and the space radiance
 . Our representation aggregates space features from a multi-level convolve
 d featurization within a conical frustum along a ray and optimizes the LoD
  feature volume through differentiable rendering. Additionally, we propose
  an error-guided sampling strategy to guide the growth of the SDF during t
 he optimization. Both qualitative and quantitative evaluations demonstrate
  that our method achieves superior surface reconstructions and photorealis
 tic view synthesis compared to state-of-the-art approaches.\n\nRegistratio
 n Category: Full Access\n\nSession Chair: Fei Hou (Institute of Software, 
 Chinese Academy of Sciences; University of Chinese Academy of Sciences)\n\
 n
URL:https://asia.siggraph.org/2023/full-program?id=papers_565&sess=sess159
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