Engineering and Technology

Non-line-of-sight imaging with arbitrary illumination and detection pattern

X. Liu, J. Wang, et al.

This groundbreaking research by Xintong Liu, Jianyu Wang, Leping Xiao, Zuoqiang Shi, Xing Fu, and Lingyun Qiu introduces a novel Bayesian framework for non-line-of-sight imaging, allowing for high-quality reconstructions even with irregular measurement patterns, which vastly broadens real-world applications.

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Abstract

This paper presents a Bayesian framework for non-line-of-sight (NLOS) imaging that overcomes limitations of existing methods by eliminating the need for dense measurements at regular grid points on the relay surface. A novel Confocal Complemented Signal-Object Collaborative Regularization (CC-SOCR) algorithm is introduced, leveraging virtual confocal signals to enable high-quality reconstructions of both albedo and surface normal, even with coarse or irregular measurement patterns. This approach significantly expands the applicability of NLOS imaging to various real-world scenarios.

Publisher

Nature Communications

Published On

Jun 03, 2023

Authors

Xintong Liu, Jianyu Wang, Leping Xiao, Zuoqiang Shi, Xing Fu, Lingyun Qiu