Nam Ji Hyun, Brandt Eric, Bauer Sebastian, Liu Xiaochun, Renna Marco, Tosi Alberto, Sifakis Eftychios, Velten Andreas
Department of Electrical and Computer Engineering, University of Wisconsin - Madison, Madison, WI, USA.
Department of Computer Science, University of Wisconsin - Madison, Madison, WI, USA.
Nat Commun. 2021 Nov 11;12(1):6526. doi: 10.1038/s41467-021-26721-x.
Non-Line-Of-Sight (NLOS) imaging aims at recovering the 3D geometry of objects that are hidden from the direct line of sight. One major challenge with this technique is the weak available multibounce signal limiting scene size, capture speed, and reconstruction quality. To overcome this obstacle, we introduce a multipixel time-of-flight non-line-of-sight imaging method combining specifically designed Single Photon Avalanche Diode (SPAD) array detectors with a fast reconstruction algorithm that captures and reconstructs live low-latency videos of non-line-of-sight scenes with natural non-retroreflective objects. We develop a model of the signal-to-noise-ratio of non-line-of-sight imaging and use it to devise a method that reconstructs the scene such that signal-to-noise-ratio, motion blur, angular resolution, and depth resolution are all independent of scene depth suggesting that reconstruction of very large scenes may be possible.
非视距(NLOS)成像旨在恢复隐藏于直接视距之外的物体的三维几何形状。该技术面临的一个主要挑战是可用的多次反射信号较弱,这限制了场景大小、捕获速度和重建质量。为克服这一障碍,我们引入了一种多像素飞行时间非视距成像方法,该方法将专门设计的单光子雪崩二极管(SPAD)阵列探测器与一种快速重建算法相结合,能够捕获并重建具有自然非后向反射物体的非视距场景的实时低延迟视频。我们建立了非视距成像的信噪比模型,并利用该模型设计了一种重建场景的方法,使得信噪比、运动模糊、角分辨率和深度分辨率均与场景深度无关,这表明重建非常大的场景是可能的。