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DTSTAMP:20240214T070245Z
LOCATION:Meeting Room C4.8\, Level 4 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231213T153500
DTEND;TZID=Australia/Melbourne:20231213T154500
UID:siggraphasia_SIGGRAPH Asia 2023_sess145_papers_837@linklings.com
SUMMARY:SeamlessNeRF: Stitching Part NeRFs with Gradient Propagation
DESCRIPTION:Technical Papers\n\nBingchen Gong and Yuehao Wang (The Chinese
  University of Hong Kong); Xiaoguang Han (Shenzhen Research Institute of B
 ig Data, the Chinese University of Hong Kong (Shenzhen)); and Qi Dou (The 
 Chinese University of Hong Kong)\n\nNeural Radiance Fields (NeRFs) have em
 erged as a promising representation for 3D scenes, sparking a surge in res
 earch aimed at extending the editing capabilities in this domain. The task
  of seamless editing and merging of different NeRFs, similar to the "copy-
 and-paste" function in 2D image editing, remains a critical operation that
  current methods struggle to accomplish. To address these challenges, we p
 ropose SeamlessNeRF, a novel approach for seamless merging and editing of 
 multiple NeRFs. Our method optimizes radiance fields within a merged NeRF 
 representation and focuses on the boundary area where different radiance f
 ields intersect, aligning radiance color and preserving the gradient field
  of the target. This technique allows for a seamless and natural fusion of
  NeRFs, while overcoming limitations faced by traditional image-based meld
 ing methods. To the best of our knowledge, SeamlessNeRF is the first to of
 fer such capabilities, advancing the field of 3D editing with an innovativ
 e gradient propagation method for radiance fields. Our method provides a r
 obust solution for complex scene composition and intricate character model
 ing, validated by extensive experimental results. Through SeamlessNeRF, we
  make the first step towards a seamless, efficient, and intuitive approach
  to editing in the realm of 3D representations.\n\nRegistration Category: 
 Full Access\n\nSession Chair: Young J. Kim (Ewha Womans University)
URL:https://asia.siggraph.org/2023/full-program?id=papers_837&sess=sess145
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