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DTSTAMP:20260114T163633Z
LOCATION:Darling Harbour Theatre\, Level 2 (Convention Centre)
DTSTART;TZID=Australia/Melbourne:20231212T093000
DTEND;TZID=Australia/Melbourne:20231212T124500
UID:siggraphasia_SIGGRAPH Asia 2023_sess209_papers_837@linklings.com
SUMMARY:SeamlessNeRF: Stitching Part NeRFs with Gradient Propagation
DESCRIPTION:Bingchen Gong and Yuehao Wang (The Chinese University of Hong 
 Kong); Xiaoguang Han (Shenzhen Research Institute of Big Data, the Chinese
  University of Hong Kong (Shenzhen)); and Qi Dou (The Chinese University o
 f Hong Kong)\n\nNeural Radiance Fields (NeRFs) have emerged as a promising
  representation for 3D scenes, sparking a surge in research aimed at exten
 ding 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 str
 uggle to accomplish. To address these challenges, we propose SeamlessNeRF,
  a novel approach for seamless merging and editing of multiple NeRFs. Our 
 method optimizes radiance fields within a merged NeRF representation and f
 ocuses on the boundary area where different radiance fields intersect, ali
 gning radiance color and preserving the gradient field of the target. This
  technique allows for a seamless and natural fusion of NeRFs, while overco
 ming limitations faced by traditional image-based melding methods. To the 
 best of our knowledge, SeamlessNeRF is the first to offer such capabilitie
 s, advancing the field of 3D editing with an innovative gradient propagati
 on method for radiance fields. Our method provides a robust solution for c
 omplex scene composition and intricate character modeling, validated by ex
 tensive experimental results. Through SeamlessNeRF, we make the first step
  towards a seamless, efficient, and intuitive approach to editing in the r
 ealm of 3D representations.\n\nRegistration Category: Full Access, Enhance
 d Access, Trade Exhibitor, Experience Hall Exhibitor\n\n
URL:https://asia.siggraph.org/2023/full-program?id=papers_837&sess=sess209
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