SNLOS: Non-line-of-sight Scanning through Temporal Focusing
SNLOS: Non-line-of-sight Scanning through Temporal Focusing
复制标题
SNLOS：通过时间聚焦进行非视距扫描
DOI：
10.1109/iccphot.2019.8747336
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
A. Veeraraghavan
中科院分区：
文献类型：
--
作者：
A. Pediredla;Akshat Dave;A. Veeraraghavan
在过去的十年里，已经开发了几种技术，通过利用光子的往返旅行时间来寻找角落。通常，这些技术需要收集具有变化的虚拟源和虚拟检测器位置的大量测量。然后通过重建算法处理该数据以估计隐藏场景。因此，即使当在隐藏体积的感兴趣的区域是小的和有限的，所需的采集时间是大的，因为整个数据集已被收购，然后processing.In本文中，我们提出了第一个例子的扫描为基础的非视线成像技术。关键思想是，如果墙上的虚拟源（脉冲源）使用二次延迟分布（非常类似于聚焦透镜的二次相位）延迟，则这些脉冲在同一时刻到达隐藏体积中的单个点-被扫描的点。在成像方面，在单个门控检测器上积分之前，对虚拟检测器应用二次延迟，使我们能够“聚焦”并扫描隐藏体积中的每个点。通过改变二次延迟曲线，我们可以将光线聚焦在隐藏体中的不同点。这提供了基于扫描的非视线成像的第一个示例，允许我们仅在感兴趣的区域中集中测量。我们推导出“时间聚焦”的理论基础，展示了令人信服的性能分析模拟，建立了一个硬件原型系统，并展示了真实的结果。
Over the last decade, several techniques have been developed for looking around the corner by exploiting the round-trip travel time of photons. Typically, these techniques necessitate the collection of a large number of measurements with varying virtual source and virtual detector locations. This data is then processed by a reconstruction algorithm to estimate the hidden scene. As a consequence, even when the region of interest in the hidden volume is small and limited, the acquisition time needed is large as the entire dataset has to be acquired and then processed.In this paper, we present the first example of scanning based non-line-of-sight imaging technique. The key idea is that if the virtual sources (pulsed sources) on the wall are delayed using a quadratic delay profile (much like the quadratic phase of a focusing lens), then these pulses arrive at the same instant at a single point in the hidden volume – the point being scanned. On the imaging side, applying quadratic delays to the virtual detectors before integration on a single gated detector allows us to ‘focus’ and scan each point in the hidden volume. By changing the quadratic delay profiles, we can focus light at different points in the hidden volume. This provides the first example of scanning based non-line-of-sight imaging, allowing us to focus our measurements only in the region of interest. We derive the theoretical underpinnings of ‘temporal focusing’, show compelling simulations of performance analysis, build a hardware prototype system and demonstrate real results.
登录
查看更多内容
DOI：
10.1109/cvpr.2017.251
发表时间：
2017-07
期刊：
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
影响因子：
--
作者：
Chia-Yin Tsai;Kiriakos N. Kutulakos;S. Narasimhan;Aswin C. Sankaranarayanan
通讯作者：
Chia-Yin Tsai;Kiriakos N. Kutulakos;S. Narasimhan;Aswin C. Sankaranarayanan
DOI：
10.1007/978-3-030-01219-9_2
发表时间：
2018
期刊：
European Conference on Computer Vision
影响因子：
--
作者：
Wang, Jian;Bartels, Joseph;Whittaker, William;Sankaranarayanan, Aswin C.;Narasimhan, Srinivasa G.
通讯作者：
Narasimhan, Srinivasa G.
影响因子：
35
作者：
Gariepy, Genevieve;Tonolini, Francesco;Faccio, Daniele
通讯作者：
Faccio, Daniele
影响因子：
56.9
作者：
DENK, W;STRICKLER, JH;WEBB, WW
通讯作者：
WEBB, WW