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赵禄达; 董骁; 徐世龙; 胡以华; 张鑫源; 钟易成

doi:10.37188/CO.2022-0186

365彩票官网

doi: 10.37188/CO.2022-0186

赵禄达 ^{1, 2,,},
董骁 ^{1, 2, 3},
徐世龙 ^{1, 2, 3},
胡以华 ^{1, 2, 3,,},
张鑫源 ^{1, 2, 3},
钟易成 ⁴

1.
国防科技大学电子对抗学院, 安徽合肥 230037
2.
国防科技大学脉冲功率国家重点实验室, 安徽合肥 230037
3.
国防科技大学电子制约技术安徽省重点实验室, 安徽合肥 230037
4.
中国人民解放军77126部队, 云南开远 661600

基金项目:国家自然科学基金（No. 61871389，No.62201597）；国防科技大学科研计划重大项目（No. ZK18-01-02，No. ZK22-35）；国防科技大学自主科研创新基金（No. 22-ZZCX-07）；国防科技创新特区项目（No. 22-TQ23-07-ZD-01-001）；军事类研究生资助课题（No. JY2021B042）；湖南省研究生科研创新项目（No. CX20220045）

详细信息

作者简介:
赵禄达（1992—），男，四川峨眉山人，博士研究生，2015年、2019年于国防科技大学分别获得学士、硕士学位，主要从事计算成像、计算机视觉、进化计算等方面的研究。E-mail：[email protected]

胡以华（1962—），男，安徽怀宁人，博士，教授，博士生导师，1988年、1997年于西北电讯工程学院和中国科学院安徽光学精密机械研究所分别获得硕士、博士学位，主要从事光电对抗理论与应用等方面的研究。E-mail：[email protected]

中图分类号:O439
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365赌球官网平台
- 收稿日期: 2022-08-24
- 录用日期: 2022-11-25
- 修回日期: 2022-09-21
- 网络出版日期: 2022-12-09

365赌球注册网站

ZHAO Lu-da ^{1, 2
,,},
DONG Xiao ^{1, 2, 3},
XU Shi-long ^{1, 2, 3},
HU Yi-hua ^{1, 2, 3
,,},
ZHANG Xin-yuan ^{1, 2, 3},
ZHONG Yi-cheng ⁴

1.
College of Electronic Engineering, National University of Defence Technology, Hefei 230037, China
2.
State Key Laboratory of Pulsed Power Laser Technology, National University of Defense Technology, Hefei 230037, China
3.
Anhui Province Key Laboratory of Electronic Restriction, National University of Defense Technology, Hefei 230037, China
4.
Unit 77126 of the PLA, Kaiyuan 661600, China

Funds:Supported by National Natural Science Foundation of China (No. 61871389, No.62201597); the Research Plan Project of the National University of Defense Technology (No. ZK18-01-02, No. ZK22-35); Independent Scientific Research Project of National University of Defense Technology (No. 22-ZZCX-07); National Defense Science and Technology Innovation Special Zone Project (No. 22-TQ23-07-ZD-01-001); Military Graduate Student Funding Priorities (No. JY2021B042); Graduate Research Innovation Project of Hunan Province (No. CX20220045)

More Information

Corresponding author: [email protected]; [email protected]

摘要

摘要:
非视域（Non-Line-of-Sight, NLoS）成像是近年来发展起来的一项新兴技术，其通过分析成像场景中的中介面信息来重建隐藏场景，实现了“拐弯成像”的效果，在多个领域有巨大的应用价值。本文主要针对NLoS成像重建算法进行综述性研究。考虑到目前NLoS成像分类存在交叉和非独立现象，本文基于物理成像模式和算法模型的不同特点，对其进行了独立的重新分类。根据提出的分类标准分别对传统和基于深度学习的NLoS成像重建算法进行了归纳总结，对代表性算法的发展现状进行了概述，推导了典型方法的实现原理，并对比了传统重建方法和基于深度学习的NLoS成像重建算法的重建应用结果。总结了NLoS成像目前存在的挑战和未来的发展方向。该研究对不同类型的NLoS成像进行了较为全面的梳理，对NLoS成像重建算法在内的一系列研究的进一步发展有着一定的支撑和推动作用。
- 非视域成像 /
- 重建算法 /
- 成像模式 /
- 深度学习
Abstract:
Non-line-of-sight (NLoS) imaging is a promising technique developed in recent years, which can reconstruct hidden scenes by analyzing the information in the intermediate surface, and "see around the corner", and has strong application value in many fields. In this paper, we review the reconstruction algorithm for NLoS imaging tasks. Firstly, considering the crossover and non-independent phenomena existing in the NLoS imaging classification, we use the different features of physical imaging models and algorithm models to reclassify them. Secondly, according to the proposed classification criteria, we respectively review the traditional and deep learning-based NLoS imaging reconstruction algorithms, summarize the state-of-the-art algorithms, and derive the implement principle. We also compare the results of deep learning-based and traditional NLoS imaging reconstruction algorithms for reconstruction tasks. Finally, the current challenges and the future development of NLoS imaging are summarized. Different types of NLoS imaging reconstruction algorithms are comprehensively analyzed in this review, which provides important support for the further development of NLoS imaging reconstruction algorithms.
- non-line-of-sight imaging /
- reconstruction algorithm /
- imaging mode /
- deep learning

HTML全文

图 1 典型的NLoS成像模式

Figure 1. Typical NLoS imaging modalities

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图 2 NLoS成像重建算法的分类

Figure 2. Classification of reconstruction algorithms of NLoS imaging

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图 3 (a) NLoS 成像的三反射光轨迹及(b)隐藏物体重建的原理示意图(改编自文献[ 8 ])

Figure 3. Schematic diagrams of (a) three reflected light trajectories of NLoS imaging and (b) hidden object reconstruction principle (adapted from Ref. [ 8 ])

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图 4 反向椭球投影重建算法的原理示意图^{[
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]}

Figure 4. Schematic diagram of the principle of the inverse ellipsoidal projection reconstruction algorithm^{[
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图 5 共焦NLoS成像的硬件配置示意图^{[
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]}

Figure 5. Schematic diagram of the hardware configuration for confocal NLoS imaging^{[
29
]}

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图 6 基于遮挡增强成像的被动NLoS成像系统示意图^{[
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]}

Figure 6. Schematic diagram of the passive NLoS imaging system based on occlusion-enhanced imaging^{[

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]}

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图 7 通过中介墙墙角阴影进行NLoS成像的示意图

Figure 7. Schematic diagram of NLoS imaging through intermediary wall corner penumbra

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图 8 基于深度学习的主动NLoS成像的实现过程示意图（改编自文献[ 62 ]）

Figure 8. Schematic diagram of the active NLoS imaging implementing process based on deep learning (adapted from Ref. [ 62 ])

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图 9 基于深度学习和光传输矩阵分解的NLoS成像的原理示意图（改编自文献[ 74 ]）

Figure 9. Schematic diagram of the NLoS imaging based on deep learning and optical transport matrices decomposition (adapted from Ref. [ 74 ])

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图 10 (a)基于深度学习的被动NLoS成像场景及(b)流行嵌入和最优传输分步生成式网络实现过程示意图（改编自文献[ 84 ]）

Figure 10. (a) Passive NLoS imaging and (b) implementing process of popular embedding and optimal transmission generated step by step based on deep learning (adapted from Ref. [ 84 ])

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图 11 使用ShapeNet和LCT算法对6种隐藏场景进行重建的结果对比^{[
62
]}

Figure 11. Comparison of reconstruction results of 6 hidden scenes using ShapeNet and LCT algorithms^{[
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]}

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图 12 使用CNN和两种PR算法（HIO、Alt-Min）对3种隐藏场景进行重建的结果对比^{[
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]}

Figure 12. Comparison of reconstruction results of 3 hidden scenes using CNN and PR algorithms (HIO and Alt-Min)^{[
72
]}

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图 13 NeTF网络结构示意图^{[
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]}

Figure 13. An illustration of the NeTF network architecture ^{[
75
]}

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图 14 使用NeTF网络与传统NLoS成像中的Phasor Field、快速F-K迁移法和DLCT算法^{[
34
]}对6种隐藏场景进行重建的结果对比^{[
75
]}

Figure 14. Comparison of reconstruction results of 6 hidden scenes using NeTF and traditional NLoS reconstruction algorithms (Phasor Field, F-K and DLCT)^{[
75
]}

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图 15 使用NLOS-OT与U-Net和C-GAN对4类隐藏场景进行重建的结果对比^{[
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Figure 15. Comparison of reconstruction results of 4 type of hidden scenes using NLOS-OT and U-Net, C-GAN^{[
80
]}

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表 1 不同种类的NLoS重建SOTA算法的多角度总结和对比分析

Table 1. A multi-perspective summary and comparative analysis of different kinds of NLoS reconstruction SOTA algorithms

算法分类			SOTA	NLoS场景中的硬件	任务	重建质量	重建速度	实际应用的差距
传统重建算法	主动 NLoS 成像	基于时间信息	①空间多路复用感知+ 压缩感知^{[ 19 ]} ②空间点扩散函数的优化^{[ 20 ]}	①数字微反射镜+SPAD ②SPAD阵列	2D重建	①好 ②较好	较快	较大
		基于光强	逆优化^{[ 39 ]}	传统相机	2D重建/跟踪/定位	一般	较快	大
		基于向量场	衍射积分法^{[ 41 ]}	SPAD	3D重建	较好	快	小
	被动 NLoS 成像	基于光强	①添加遮挡的优化^{[ 48 ]} ②优化墙角阴影^{[ 52 ]}	传统相机	①2D重建 ②2D重建/定位	①较好 ②好	一般	大
		基于偏振性	逆优化^{[ 56 ]}	偏光器+传统相机	2D重建	一般	快	大
		基于相干性	双谱＋相位检索^{[ 58 ]}	遮挡板＋阵列相机	2D重建	一般	快	大
基于深度学习的重建算法	主动 NLoS 成像	基于端到端学习	快速光场断层扫描+ 深度神经网络^{[ 64 ]}	条纹相机	3D重建	较好	很快	小
	主动 NLoS 成像	物理和深度学习模型融合	①神经瞬态场^{[ 75 ]} ②逆矩阵生成+ 深度神经网络^{[ 74 ]}	①SPAD ②传统相机	①3D重建 ②2D重建	好	一般	较大
	被动NLoS 成像	基于端到端学习	最有传输理论+ 深度神经网络^{[ 80 ]}	传统相机	2D重建	很好	快	小

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