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Confocal non-line-of-sight imaging based on the light-cone transform
Nature ( IF 64.8 ) Pub Date : 2018-03-01 , DOI: 10.1038/nature25489
Matthew O’Toole , David B. Lindell , Gordon Wetzstein

How to image objects that are hidden from a camera’s view is a problem of fundamental importance to many fields of research, with applications in robotic vision, defence, remote sensing, medical imaging and autonomous vehicles. Non-line-of-sight (NLOS) imaging at macroscopic scales has been demonstrated by scanning a visible surface with a pulsed laser and a time-resolved detector. Whereas light detection and ranging (LIDAR) systems use such measurements to recover the shape of visible objects from direct reflections, NLOS imaging reconstructs the shape and albedo of hidden objects from multiply scattered light. Despite recent advances, NLOS imaging has remained impractical owing to the prohibitive memory and processing requirements of existing reconstruction algorithms, and the extremely weak signal of multiply scattered light. Here we show that a confocal scanning procedure can address these challenges by facilitating the derivation of the light-cone transform to solve the NLOS reconstruction problem. This method requires much smaller computational and memory resources than previous reconstruction methods do and images hidden objects at unprecedented resolution. Confocal scanning also provides a sizeable increase in signal and range when imaging retroreflective objects. We quantify the resolution bounds of NLOS imaging, demonstrate its potential for real-time tracking and derive efficient algorithms that incorporate image priors and a physically accurate noise model. Additionally, we describe successful outdoor experiments of NLOS imaging under indirect sunlight.

中文翻译:

基于光锥变换的共焦非视线成像

如何对隐藏在相机视野中的物体进行成像是许多研究领域的根本问题,在机器人视觉、国防、遥感、医学成像和自动驾驶汽车中都有应用。宏观尺度的非视线 (NLOS) 成像已经通过用脉冲激光和时间分辨探测器扫描可见表面来证明。光探测和测距 (LIDAR) 系统使用此类测量从直接反射中恢复可见物体的形状,而 NLOS 成像则从多重散射光中重建隐藏物体的形状和反照率。尽管最近取得了进展,但由于现有重建算法的内存和处理要求过高,以及多重散射光信号极弱,NLOS 成像仍然不切实际。在这里,我们表明共聚焦扫描程序可以通过促进光锥变换的推导来解决这些挑战,以解决 NLOS 重建问题。这种方法需要比以前的重建方法少得多的计算和内存资源,并且以前所未有的分辨率对隐藏对象进行图像处理。在对逆向反射物体成像时,共聚焦扫描还提供了相当大的信号和范围增加。我们量化了 NLOS 成像的分辨率界限,展示了其实时跟踪的潜力,并推导出了包含图像先验和物理精确噪声模型的高效算法。此外,我们还描述了在间接阳光下进行 NLOS 成像的成功户外实验。
更新日期:2018-03-01
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