Deep Surface Light Fields
Anpei Chen1; Minye Wu1; Yingliang Zhang1; Nianyi Li2; Jie Lu1; Shenghua Gao1; Jingyi Yu1
2018-07-01
Source PublicationPROC. ACM COMPUT. GRAPH. INTERACT. TECH.
ISSN2577-6193
Volume1Issue:1Pages:14:1--14:17
Status已发表
DOI10.1145/3203192
Abstract

A surface light field represents the radiance of rays originating from any points on the surface in any directions. Traditional approaches require ultra-dense sampling to ensure the rendering quality. In this paper, we present a novel neural network based technique called deep surface light field or DSLF to use only moderate sampling for high fidelity rendering. DSLF automatically fills in the missing data by leveraging different sampling patterns across the vertices and at the same time eliminates redundancies due to the network's prediction capability. For real data, we address the image registration problem as well as conduct texture-aware remeshing for aligning texture edges with vertices to avoid blurring. Comprehensive experiments show that DSLF can further achieve high data compression ratio while facilitating real-time rendering on the GPU.

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Language英语
PublisherACM
Citation statistics
Cited Times:17[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttps://kms.shanghaitech.edu.cn/handle/2MSLDSTB/28945
Collection信息科学与技术学院_硕士生
信息科学与技术学院_PI研究组_虞晶怡组
信息科学与技术学院_PI研究组_高盛华组
信息科学与技术学院_博士生
Affiliation1.ShanghaiTech University, Shanghai, SH, China
2.Duke University, USA
First Author AffilicationShanghaiTech University
First Signature AffilicationShanghaiTech University
Recommended Citation
GB/T 7714
Anpei Chen,Minye Wu,Yingliang Zhang,et al. Deep Surface Light Fields[J]. PROC. ACM COMPUT. GRAPH. INTERACT. TECH.,2018,1(1):14:1--14:17.
APA Anpei Chen.,Minye Wu.,Yingliang Zhang.,Nianyi Li.,Jie Lu.,...&Jingyi Yu.(2018).Deep Surface Light Fields.PROC. ACM COMPUT. GRAPH. INTERACT. TECH.,1(1),14:1--14:17.
MLA Anpei Chen,et al."Deep Surface Light Fields".PROC. ACM COMPUT. GRAPH. INTERACT. TECH. 1.1(2018):14:1--14:17.
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