Imaging objects outside a camera’s direct line of sight has important applications in robotic vision, remote sensing, and many other domains. Time-of-flight-based non-line-of-sight (NLOS) imaging systems have recently demonstrated impressive results, but several challenges remain. Image formation and inversion models have been slow or limited by the types of hidden surfaces that can be imaged. Moreover, non-planar sampling surfaces and non-confocal scanning methods have not been supported by efficient NLOS algorithms. With this work, we introduce a wave-based image formation model for the problem of NLOS imaging. Inspired by inverse methods used in seismology, we adapt a frequency-domain method, f-k migration, for solving the inverse NLOS problem. Unlike existing NLOS algorithms, f-k migration is both fast and memory efficient, it is robust to specular and other complex reflectance properties, and we show how it can be used with non-confocally scanned measurements as well as for non-planar sampling surfaces. f-k migration is more robust to measurement noise than alternative methods, generally produces better quality reconstructions, and is easy to implement. We experimentally validate our algorithms with a new NLOS imaging system that records room-sized scenes outdoors under indirect sunlight, and scans persons wearing retroreflective clothing at interactive rates.
Bibliographical noteKAUST Repository Item: Exported on 2022-06-21
Acknowledgements: The authors thank Ioannis Gkioulekas for his helpful feedback. This project was supported by a Stanford Graduate Fellowship, an NSF CAREER Award (IIS 1553333), a Terman Faculty Fellowship, a Sloan Fellowship, by the KAUST Office of Sponsored Research through the Visual Computing Center CCF grant, the DARPA REVEAL program, and the ARO (ECASE-Army Award W911NF-19-1-0120).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.