## 1 Introduction  
LiDAR (light detection and ranging) plays a pivotal role in intelligent robots, autonomous vehicles, and unmanned aerial vehicles (UAVs), as it provides precise distance measurements of the surrounding environments, making it one of the most essential sensors. It is fundamental to the operation of these advanced technologies, enabling them to navigate safely, avoid obstacles, and make informed decisions about their movements. Without LiDAR, these machines would lack the necessary data to function properly, severely limiting their capabilities and reliability. Therefore, LiDAR's role in fields such as robotics, autonomous driving, and unmanned aerial vehicles is crucial, making it a vital component for the development of advanced and safe technologies [1, 2]. Currently, LiDAR sensors available off-the-shelf can be classified into two main categories: mechanical LiDAR and solid-state LiDAR [3]. Over the past few decades, mechanical LiDAR has been extensively utilized in various applications and environments because of its design, which incorporates a 360-degree horizontal field of view (FOV). This broad FOV allows mechanical LiDAR to capture comprehensive information about the surrounding environment within a single scan. The vertical FOV is determined by the number of included laser beams and the angle between adjacent laser beams, which is also referred to as angular resolution. Mechanical LiDAR offers numerous advantages, such as its ability to obtain all surrounding information within a single scan. This design enables it to acquire data quickly and efficiently, making it suitable for a range of applications, including autonomous vehicles, robotics, and surveying. However, the predefined angular resolutions in both the horizontal and vertical directions can limit the resolution of perception, particularly in the horizontal angular resolution. As the distance increases, the spacing between each laser beam also increases, resulting in sparse features in the final 3D point cloud. To address these limitations, solid-state LiDAR was developed using non-repetitive scanning technology. This technology concentrates all the laser beams within a limited field of view, enabling dense scanning over a specific time interval. By focusing the laser beams in a limited FOV, solid-state LiDAR can achieve dense scanning without sacrificing the overall field of view range. This approach offers improved resolution compared to mechanical LiDAR, as it utilizes non-repetitive scanning patterns. Despite its advantages, solid-state LiDAR also has certain trade-offs. To achieve non-repetitive scanning, complex mechanical structures are required, which can reduce the final ranging accuracy of the sensor. Additionally, the limited field of view can be a constraint in some applications where a wide FOV is essential. To achieve a full 360-degree horizontal sensing capability, multiple LiDAR sensors, sophisticated installation structures, and synchronization algorithms are often required [4]. To overcome the shortcomings of mechanical LiDAR in practical applications, we delve into recent advances in point cloud [5; 6; 7], image-based super-resolution technology [8; 9], and deep-learning-based modeling [10; 11], which can provide new ideas and methods to solve the limitations of mechanical LiDAR. First of all, as the main data form of LiDAR environment perception, the point cloud has accurate 3D information. However, because of the scanning method of mechanical LiDAR, the obtained point clouds are usually very sparse, which poses challenges for subsequent algorithms. Therefore, we consider using point cloud super-resolution technology. By using multi-frame continuous LiDAR scanning data combined with image processing and machine learning methods, the point cloud is interpolated and upsampled. This can improve the density of the point cloud to a certain extent and provide more accurate environmental information for the subsequent perception algorithm.