Non-line-of-Sight Imaging via Neural Transient Fields
文献类型：期刊论文
| 作者 | Zhong, Zhao1 ; Yang, Zichen2; Deng, Boyang2; Yan, Junjie2; Wu, Wei2; Shao, Jing2; Liu, Cheng-Lin3,4
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| 刊名 | IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
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| 出版日期 | 2021-07-01 |
| 卷号 | 43期号:7页码:2314-2328 |
| 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 |
| ISSN号 | 0162-8828 |
| DOI | 10.1109/TPAMI.2020.2969193 |
| 通讯作者 | Liu, Cheng-Lin(liucl@nlpr.ia.ac.cn) |
| 英文摘要 | 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. |
| 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] |
| WOS研究方向 | Computer Science ; Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000659549700011 |
| 出版者 | IEEE COMPUTER SOC |
| 资助机构 | Major Project for New Generation of AI ; National Natural Science Foundation of China (NSFC) |
| 源URL | [http://ir.ia.ac.cn/handle/173211/45361]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_模式分析与学习团队 |
| 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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