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DeferredGS: Decoupled and Relightable Gaussian Splatting With Deferred Shading

文献类型：期刊论文

作者Wu, Tong2,3; Sun, Jia-Mu2,3; Lai, Yu-Kun4; Ma, Yuewen1; Kobbelt, Leif5; Gao, Lin2,3
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2025-08-01
卷号47期号:8页码:6307-6319
关键词Lighting Rendering (computer graphics) Geometry Three-dimensional displays Neural radiance field Training Solid modeling Surface reconstruction Harmonic analysis Image reconstruction Gaussian splatting inverse rendering editing
ISSN号0162-8828
DOI10.1109/TPAMI.2025.3560933
英文摘要Reconstructing and editing 3D objects and scenes both play crucial roles in computer graphics and computer vision. Neural radiance fields (NeRFs) can achieve realistic reconstruction and editing results but suffer from inefficiency in rendering. Gaussian splatting significantly accelerates rendering by rasterizing Gaussian ellipsoids. However, Gaussian splatting utilizes a single Spherical Harmonic (SH) function to model both texture and lighting, limiting independent editing capabilities of these components. Recently, attempts have been made to decouple texture and lighting with the Gaussian splatting representation but may fail to produce plausible geometry and decomposition results on reflective scenes. Additionally, the forward shading technique they employ introduces noticeable blending artifacts during relighting, as the geometry attributes of Gaussians are optimized under the original illumination and may not be suitable for novel lighting conditions. To address these issues, we introduce DeferredGS, a method for decoupling and relighting the Gaussian splatting representation using deferred shading. To achieve successful decoupling, we model the illumination with a learnable environment map and define additional attributes such as texture parameters and normal direction on Gaussians, where the normal is distilled from a jointly trained signed distance function. More importantly, we apply deferred shading, resulting in more realistic relighting effects compared to previous methods. Both qualitative and quantitative experiments demonstrate the superior performance of DeferredGSin novel view synthesis and relighting tasks.
资助项目Beijing Municipal Science and Technology Commission[Z231100005923031] ; National Natural Science Foundation of China[62322210] ; Innovation Funding of ICT, CAS[E461020]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001522958700047
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/41758]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Gao, Lin
作者单位1.ByteDance Pico, Beijing 100098, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100045, Peoples R China
3.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
4.Cardiff Univ, Sch Comp Sci & Informat, Cardiff CF10 3AT, Wales
5.Rhein Westfal TH Aachen, D-52062 Aachen, Germany
推荐引用方式
GB/T 7714		 Wu, Tong,Sun, Jia-Mu,Lai, Yu-Kun,et al. DeferredGS: Decoupled and Relightable Gaussian Splatting With Deferred Shading[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2025,47(8):6307-6319.
APA Wu, Tong,Sun, Jia-Mu,Lai, Yu-Kun,Ma, Yuewen,Kobbelt, Leif,&Gao, Lin.(2025).DeferredGS: Decoupled and Relightable Gaussian Splatting With Deferred Shading.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,47(8),6307-6319.
MLA Wu, Tong,et al."DeferredGS: Decoupled and Relightable Gaussian Splatting With Deferred Shading".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 47.8(2025):6307-6319.

入库方式： OAI收割

来源：计算技术研究所

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