Conditional visibility aware view synthesis via parallel light fields

doi:10.1016/j.neucom.2024.127644

CORC > 自动化研究所 > 中国科学院自动化研究所 > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化团队

	Conditional visibility aware view synthesis via parallel light fields
	Shen, Yu 4,5; Li, Yuke 1,3; Liu, Yuhang 4; Wang, Yutong 4; Chen, Long 3,4; Wang, Fei-Yue 2,4,5
刊名	NEUROCOMPUTING
	2024-07-01
卷号	588 页码:13
关键词	Parallel theory Light fields Neural rendering View synthesis Conditional visibility Normalizing Flow
ISSN号	0925-2312
DOI	10.1016/j.neucom.2024.127644
通讯作者	Li, Yuke(liyuke14@mails.ucas.ac.cn)
英文摘要	In the area of neural rendering -based novel view synthesis, illumination is important since shadows cast by objects under various light sources provide indications about their geometries and materials. However, due to high physical device complexity and simulation distortion, large-scale photorealistic multiple illumination multi -view datasets are difficult to obtain. In order to address this problem, a physical -virtual interactive parallel light fields based collection method is proposed in this paper. The physical part of parallel light fields is firstly used to capture 3D models and 2D images of objects under different lights. Then a Reakto-Sim adaptation module was proposed to enhance realism by estimating material characteristic. Instead of manually setting, the learned resulting material parameters are then utilized to initialize virtual engine blender for subsequent rendering and data collection. Besides, to better handle self -occlusion problem in the acquired parallel light fields dataset, a conditional visibility module is designed in modeling visibility of each sampling point along a sampling ray. Compared with the Neuray, by introducing Conditional Normalizing Flow, visibility are assumed as samples from some distribution due to the fact that visibilities along the ray should be monotonically decreasing and are within the range of [0 , 1] . The visibility are calculated in a data driven manner, which brings more flexibility. By pretraining the conditional visibility network in parallel light field dataset, experiments demonstrate that more photorealistic inputs improve Peak -Signal -Noise Ratio (PSNR) and Structure Similarity Index Measure (SSIM) by 0.11% and 0.68% in validation dataset NeRF synthesis and LLFF. Besides, compared to Neuray, the proposed conditional visibility module is more flexible and get a PSNR improvement of 0.55 and 0.5 in NeRF synthesis and LLFF dataset, respectively.
资助项目	Key Research and Developing Program 2020 of Guangzhou[202007050002] ; Developing Program of Guangdong Province[2020B090921003] ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV)
WOS关键词	NEURAL RADIANCE FIELDS ; INTELLIGENCE ; NETWORK ; SYSTEM
WOS研究方向	Computer Science
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:001229948200001
资助机构	Key Research and Developing Program 2020 of Guangzhou ; Developing Program of Guangdong Province ; Intel Collaborative Research Institute for Intelligent and Automated Connected Vehicles (ICRI-IACV)
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/58418]
专题	自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者	Li, Yuke
作者单位	1.Northwestern Polytech Univ, Sch Software, Xian 710072, Peoples R China 2.Macau Univ Sci & Technol, Macao Inst Syst Engn, Macau 999078, Peoples R China 3.Waytous Co Ltd, Beijing 100083, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式 GB/T 7714	Shen, Yu,Li, Yuke,Liu, Yuhang,et al. Conditional visibility aware view synthesis via parallel light fields[J]. NEUROCOMPUTING,2024,588:13.
APA	Shen, Yu,Li, Yuke,Liu, Yuhang,Wang, Yutong,Chen, Long,&Wang, Fei-Yue.(2024).Conditional visibility aware view synthesis via parallel light fields.NEUROCOMPUTING,588,13.
MLA	Shen, Yu,et al."Conditional visibility aware view synthesis via parallel light fields".NEUROCOMPUTING 588(2024):13.