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。