Non-line-of-Sight Imaging via Neural Transient Fields

doi:10.1109/TPAMI.2020.2969193

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 模式分析与学习

	Non-line-of-Sight Imaging via Neural Transient Fields
	Zhong, Zhao1 ; Yang, Zichen 2; Deng, Boyang 2; Yan, Junjie 2; Wu, Wei 2; Shao, Jing 2; Liu, Cheng-Lin3,4
发表期刊	IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
ISSN	0162-8828
	2021-07-01
卷号	43 期号:7 页码:2314-2328
通讯作者	Liu, Cheng-Lin(liucl@nlpr.ia.ac.cn)
摘要	We present a neural modeling framework for non-line-of-sight (NLOS) imaging. Previous solutions have sought to explicitly recover the 3D geometry (e.g., as point clouds) or voxel density (e.g., within a pre-defined volume) of the hidden scene. In contrast, inspired by the recent Neural Radiance Field (NeRF) approach, we use a multi-layer perceptron (MLP) to represent the neural transient field or NeTF. However, NeTF measures the transient over spherical wavefronts rather than the radiance along lines. We therefore formulate a spherical volume NeTF reconstruction pipeline, applicable to both confocal and non-confocal setups. Compared with NeRF, NeTF samples a much sparser set of viewpoints (scanning spots) and the sampling is highly uneven. We thus introduce a Monte Carlo technique to improve the robustness in the reconstruction. Experiments on synthetic and real datasets demonstrate NeTF achieves state-of-the-art performance and can provide reliable reconstructions even under semi-occlusions and on non-Lambertian materials.
关键词	Transient analysis Image reconstruction Imaging Nonlinear optics Measurement by laser beam Surface reconstruction Solid modeling Computational photography non-line-of-sight imaging neural radiance field neural rendering
DOI	10.1109/TPAMI.2020.2969193
收录类别	SCI
语种	英语
资助项目	Major Project for New Generation of AI[2018AAA0100400] ; National Natural Science Foundation of China (NSFC)[61721004] ; National Natural Science Foundation of China (NSFC)[61633021]
项目资助者	Major Project for New Generation of AI ; National Natural Science Foundation of China (NSFC)
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000659549700011
出版者	IEEE COMPUTER SOC
七大方向——子方向分类	图像视频处理与分析
引用统计	被引频次：6[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/45362
专题	多模态人工智能系统全国重点实验室_模式分析与学习
通讯作者	Liu, Cheng-Lin
作者单位	1.Univ Chinese Acad Sci, Chinese Acad Sci, Inst Automat, NLPR, Beijing 100190, Peoples R China 2.SenseTime Grp Ltd, Sensetime Res Inst, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, NLPR, Beijing, Peoples R China 4.Univ Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
第一作者单位	模式识别国家重点实验室
通讯作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Zhong, Zhao,Yang, Zichen,Deng, Boyang,et al. Non-line-of-Sight Imaging via Neural Transient Fields[J]. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,2021,43(7):2314-2328.
APA	Zhong, Zhao.,Yang, Zichen.,Deng, Boyang.,Yan, Junjie.,Wu, Wei.,...&Liu, Cheng-Lin.(2021).Non-line-of-Sight Imaging via Neural Transient Fields.IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE,43(7),2314-2328.
MLA	Zhong, Zhao,et al."Non-line-of-Sight Imaging via Neural Transient Fields".IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 43.7(2021):2314-2328.