# Retina-like Imaging and Its Applications: A Brief Review
## 3 Applications
### Three-Dimensional Acquisition and Reconstruction  
The technology of 3D image acquisition and reconstruction describes the real scenario into the mathematical model, which conforms to the logic expression of computers, and these models play an auxiliary role in many research fields, including historical preservation [36], game development [37], architectural design [38], and clinical medicine [39; 40]. The emphasis of 3D reconstruction technology is to obtain the target scenario or the depth and texture information of the object. The methods of retina-like technology result in a great number of interests and considerable attention owing to it fully combining the advantages of variable resolution, anti-rotation, and scale transformation. Figure 4 shows the retina-like LiDAR. It obtains the 3D figure using the time-of-flight principle, and the retina-like adaptive spacial invariance scanning method makes it more flexible. The 3D reconstruction is conducted by the use of LPT [33], and the original input image (Figure 5a) is divided into different regions (Figure 5b) according to linear detection in log-polar coordinates. The main line of (Figure 5a) is found through image processing, and it is divided into front wall, right wall, ceiling, left wall, floor, and right wall. Then, the obtained line is mapped to the inverse log-polar coordinates and fused with the original image. The optical center of the image is acquired via finding the vanishing point (i.e., the intersection points of the line). Finally, the depth information of the image is extracted according to the position of the optical center, and the 3D image reconstruction is realized. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Implementation** & \multicolumn{2}{c}{**Hardware**} & \multicolumn{1}{c}{**Software**} \\ \cline{2-4}
**Methods** & **Passive** & **Active** & \\ \hline \multirow{2}{*}{Characteristics} & Selectively receive image information & Purposeful information & Data compression \\  & Realize local high & & \\  & resolution, good & & \\  & scalability & & \\  & volume & & \\  & More improvement & & \\  & on imaging devices & & cooperation & performance \\  & Difficult & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different methods. Figure 4: Retina-like LiDAR based on MEMS. [PERSON] et al. [41] proposed a robust adaptive focusing measurement operator based on the biological inspiration, data selection, and edge in-variance characteristics of LPT, which was widely used in 3D shape restoration. The signal-to-noise ratio (SNR) of the image is improved by the use of LPT, and a better corresponding focus plane is obtained, which determines 3D shapes more accurately. Experiments on simulated target and real target image sequences indicated that the robust adaptive focusing measurement operator is effective in the presence of various types of noise, including high noise variance or strong noise density. Image registration is a necessary part of 3D reconstruction. [PERSON] et al. [42] studied a multi-modal image registration algorithm based on LPT, which conducts in-plane translation, rotation, and out-plane translation by using LPT. Large initial displacement registration of 3D CT images and 2D single-plane fluoroscopic images is successfully performed. [PERSON] et al. [43] built a scale, rotation, and translation-invariant descriptor based on the LPT of Gabor filter derivatives for the automatic co-registration of 3D multi-sensor point clouds. The co-registration process using data from a study area in Toronto, Ontario, Canada, which consists of a building surrounded by vegetation, bare land, paved roads, and parking lots, is shown in Figure 6. The source and target point clouds are collected from unmanned aerial vehicles and mobile laser scanning, respectively.