Recently, there has been increased attention towards 3D imaging using
single-pixel single-photon detection (also known as temporal data) due to its
potential advantages in terms of cost and power efficiency. However, to
eliminate the symmetry blur in the reconstructed images, a fixed background is
required. This paper proposes a fusion-data-based 3D imaging method that
utilizes a single-pixel single-photon detector and a millimeter-wave radar to
capture temporal histograms of a scene from multiple perspectives.
Subsequently, the 3D information can be reconstructed from the one-dimensional
fusion temporal data by using Artificial Neural Network (ANN). Both the
simulation and experimental results demonstrate that our fusion method
effectively eliminates symmetry blur and improves the quality of the
reconstructed images.

提出了一种基于融合数据的三维成像方法，利用单像素单光子探测器和毫米波雷达从多个角度捕捉场景的时间直方图，通过使用人工神经网络从一维融合时序数据中重建三维信息，成功消除对称模糊并提高了重建图像的质量。

基于单光子探测器和毫米波雷达融合时序数据的单像素三维成像

Single-pixel 3D imaging based on fusion temporal data of single photon  detector and millimeter-wave radar

A remote-sensing system that can determine the position of hidden objects has
applications in many critical real-life scenarios, such as search and rescue
missions and safe autonomous driving. Previous work has shown the ability to
range and image objects hidden from the direct line of sight, employing
advanced optical imaging technologies aimed at small objects at short range. In
this work we demonstrate a long-range tracking system based on single laser
illumination and single-pixel single-photon detection. This enables us to track
one or more people hidden from view at a stand-off distance of over 50~m. These
results pave the way towards next generation LiDAR systems that will
reconstruct not only the direct-view scene but also the main elements hidden
behind walls or corners.

本研究设计了一种基于单激光照射和单像素单光子探测的长距离追踪系统，能够在 50m 的距离上追踪一个或多个隐藏在视野之外的人，为下一代 LiDAR 系统的发展铺平了道路。