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NeRF-Texture: Synthesizing Neural Radiance Field Textures

文献类型：期刊论文

作者Huang, Yi-Hua1,2; Cao, Yan-Pei3; Lai, Yu-Kun4; Shan, Ying5; Gao, Lin1,2
刊名IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
出版日期2024-09-01
卷号46期号:9页码:5986-6000
关键词Shape Rendering (computer graphics) Surface texture Feature extraction Geometry Optimization Image reconstruction Meso-structure texture neural radiance fields texture synthesis
ISSN号0162-8828
DOI10.1109/TPAMI.2024.3382198
英文摘要Texture synthesis is a fundamental problem in computer graphics that would benefit various applications. Existing methods are effective in handling 2D image textures. In contrast, many real-world textures contain meso-structure in the 3D geometry space, such as grass, leaves, and fabrics, which cannot be effectively modeled using only 2D image textures. We propose a novel texture synthesis method with Neural Radiance Fields (NeRF) to capture and synthesize textures from given multi-view images. In the proposed NeRF texture representation, a scene with fine geometric details is disentangled into the meso-structure textures and the underlying base shape. This allows textures with meso-structure to be effectively learned as latent features situated on the base shape, which are fed into a NeRF decoder trained simultaneously to represent the rich view-dependent appearance. Using this implicit representation, we can synthesize NeRF-based textures through patch matching of latent features. However, inconsistencies between the metrics of the reconstructed content space and the latent feature space may compromise the synthesis quality. To enhance matching performance, we further regularize the distribution of latent features by incorporating a clustering constraint. In addition to generating NeRF textures over a planar domain, our method can also synthesize NeRF textures over curved surfaces, which are practically useful. Experimental results and evaluations demonstrate the effectiveness of our approach.
资助项目Beijing Municipal Natural Science Foundation for Distinguished Young Scholars[JQ21013] ; National Natural Science Foundation of China[62322210] ; Beijing Municipal Science and Technology Commission[Z231100005923031]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001290498900037
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/39647]  
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.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp, Pervas Device, Beijing 100045, Peoples R China
2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
3.VAST, Campbell, CA 95008 USA
4.Cardiff Univ, Sch Comp Sci & Informat, Cardiff CF24 4AG, Wales
5.Tencent PCG, ARC Lab, Beijing 100084, Peoples R China
推荐引用方式
GB/T 7714		 Huang, Yi-Hua,Cao, Yan-Pei,Lai, Yu-Kun,et al. NeRF-Texture: Synthesizing Neural Radiance Field Textures[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2024,46(9):5986-6000.
APA Huang, Yi-Hua,Cao, Yan-Pei,Lai, Yu-Kun,Shan, Ying,&Gao, Lin.(2024).NeRF-Texture: Synthesizing Neural Radiance Field Textures.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,46(9),5986-6000.
MLA Huang, Yi-Hua,et al."NeRF-Texture: Synthesizing Neural Radiance Field Textures".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 46.9(2024):5986-6000.

入库方式： OAI收割

来源：计算技术研究所

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