  Single-pixel imaging (SPI) is significant for applications constrained by
transmission bandwidth or lighting band, where 3D SPI can be further realized
through capturing signals carrying depth. Sampling strategy and reconstruction
algorithm are the key issues of SPI. Traditionally, random patterns are often
adopted for sampling, but this blindly passive strategy requires a high
sampling rate, and even so, it is difficult to develop a reconstruction
algorithm that can maintain higher accuracy and robustness. In this paper, an
active strategy is proposed to perform sampling with targeted scanning by
designed patterns, from which the spatial information can be easily reordered
well. Then, deep learning methods are introduced further to achieve 3D
reconstruction, and the ability of deep learning to reconstruct desired
information under low sampling rates are analyzed. Abundant experiments verify
that our method improves the precision of SPI even if the sampling rate is very
low, which has the potential to be extended flexibly in similar systems
according to practical needs.
