## 1 Introduction  
Three-dimensional (3D) imaging and multi-pixel range-finding constitute some of the most important and innovative fields in the science and engineering of image sensors in the past decades [1], where the time-of-flight (ToF) camera is one of most-used 3D imaging techniques, due to its characteristics of a miniaturized structure, low cost, and direct readout of depth images [2]. Though the principle of the ToF has been proposed by [PERSON] since the 17 th century, the first generation of ToF cameras was invented after the important parameters for the cameras' performance were precisely defined in the early 1990s [3]. With the development of various supporting techniques, many cost-efficient solutions to the ToF cameras have been implemented, and some limitations have been resolved in different imaging scenarios, such as transient imaging [4; 5; 6], multi-view stereo methods [7], calculation of the 3D motion speed of an object [8], and the transient capture of light [6; 9]. In recent years, some new methods were proposed to acquire 3D images by combining the ToF methods with single-pixel imaging [10; 11; 12]. Generally, the ToF measurement techniques can be mainly classified in two categories, i.e., so-called direct-ToF (D-ToF) and indirect-ToF (I-ToF),according to the measurement methods of the round-trip time of optical signals [13; 14], where both pulsed and continuous wave modulated light sources in each technique are most commonly used [15]. For example, [PERSON] and [PERSON]'s pioneering work on electronics-based sensors for 3D imaging is a pulsed I-ToF technique [16], while [PERSON]'s group gave a solution for high-precision 3D imaging in real-time used a continuous wave modulated I-ToF technique [17]. Additionally, single photon avalanche diodes (SPADs) are detectors capable of capturing individual photons with very high time-of-arrival resolution, in the order of a few tens of picoseconds. By reason of their picoseconds timing resolution, SPADs have become a natural candidate for D-ToF techniques [18; 19; 20; 21] since it was first proposed by [PERSON] and [PERSON] in 2002 [22]. Compared with the other 3D imaging methods, such as structured light 3D imaging [23; 24; 25; 26; 27] and the binocular vision 3D imaging [28], ToF cameras can acquire both the depth and intensity information of a scene in real time [29], which greatly enhance its application prospects. Therefore, several ToF cameras have been widely applied in both some commercial and research fields, such as multi-media, consumer electronics, security/surveillance [30], super-resolution imaging [31; 32], non-line-of-sight imaging [33], and autonomous driving [34]. However, though great progress has been made for ToF cameras, the low pixel resolution and signal-to-noise ratio (SNR) in these applications are still bottlenecks that have not been broken through so far [35]. Computational ghost imaging (CGI), which was theoretically proposed by [PERSON] [36] more than a decade ago, is based on ghost imaging (GI) modality [37; 38], where only a detection beam actually exists and is captured by a single-pixel bucket detector in the detection plane, while the optical intensity distribution in the reference beam can be obtained by data processing. Therefore, a ghost image of a target object can be reconstructed by the intensity (fluctuation) correlation between the bucket and reference signals, just like a traditional GI does, which is even also available in harsh environments such as atmospheric turbulence [39] or scattering media [40], where traditional imaging methods do not work. Fortunately, given the success of the compressed sensing (CS) [31; 41; 42; 43] algorithm in GI, CS is a naturally suitable algorithm for computational ghost image reconstructions for sparse targets, because CGI has the same core mechanics with GI [44; 45; 46; 47; 48; 49; 50; 51].