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13 May 2019 A line-of-sight approach for non-line-of-sight imaging (Conference Presentation)
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Abstract
Standard imaging systems, such as cameras, radars and lidars, are becoming a big part of our everyday life when it comes to detection, tracking and recognition of targets that are in the direct line-of-sight (LOS) of the imaging system. Challenges however start to arise when the objects are not in the system’s LOS, typically when an occluder is obstructing the imager’s field of view. This is known as non-line-of-sight (NLOS) and it is approached in different ways depending on the imager’s operating wavelength. We consider an optical imaging system and the literature offers different approaches from a component and recovery algorithm point of view. In our optical setup, we assume a system comprising an ultra-fast laser and a single photon avalanche diode (SPAD). The former is used to sequentially illuminate different points on a diffuser (relay) wall, causing the photons to uniformly scatter in all directions, including the target’s location. The latter component collects the scattered photons as a function of time. In post-processing, back-projection based algorithms are employed to recover the target’s image. Recent publications focused their attention on showing the quality of the results, as well as potential algorithm improvements. Here we show results based on a novel theoretical approach (coined as “phasor fields”), which suggests treating the NLOS imaging problem as a LOS one. The key feature is to consider the relay wall as a virtual sensor, created by the different points illuminated on the wall. Results show the superiority of this method compared to standard approaches.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Marco La Manna, Xioachun Liu, Ji-Hyun Nam, Martin Laurenzis, and Andreas Velten "A line-of-sight approach for non-line-of-sight imaging (Conference Presentation)", Proc. SPIE 10990, Computational Imaging IV, 1099005 (13 May 2019); https://doi.org/10.1117/12.2519002
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