  As an emerging technology that has attracted huge attention,
non-line-of-sight (NLOS) imaging can reconstruct hidden objects by analyzing
the diffuse reflection on a relay surface, with broad application prospects in
the fields of autonomous driving, medical imaging, and defense. Despite the
challenges of low signal-to-noise ratio (SNR) and high ill-posedness, NLOS
imaging has been developed rapidly in recent years. Most current NLOS imaging
technologies use conventional physical models, constructing imaging models
through active or passive illumination and using reconstruction algorithms to
restore hidden scenes. Moreover, deep learning algorithms for NLOS imaging have
also received much attention recently. This paper presents a comprehensive
overview of both conventional and deep learning-based NLOS imaging techniques.
Besides, we also survey new proposed NLOS scenes, and discuss the challenges
and prospects of existing technologies. Such a survey can help readers have an
overview of different types of NLOS imaging, thus expediting the development of
seeing around corners.
