# Rapid Target Extraction in LiDAR Sensing and Its Application in Rocket Launch Phase Measurement
## 1 Introduction  
LiDAR (Light Detection and Ranging), as an active remote sensing technology, has found extensive applications in various domains such as aerospace [1], autonomous driving [2, 3], geographic information systems (GIS) [4, 5], and unmanned aerial vehicle (UAV) surveying [6, 7, 8] in recent years. With the ongoing advancements in LiDAR technology, the volume and complexity of the acquired data have significantly increased, thereby imposing more stringent requirements on processing efficiency. This is especially critical in application scenarios such as real-time environmental perception, dynamic target detection, and autonomous navigation, where the need to efficiently and accurately process vast amounts of point cloud data has become a central research challenge. LiDAR is capable of efficiently capturing 3D point clouds of target scenes; however, the time-consuming nature of its data processing remains a persistent challenge. With the enhancement of MEMS (Micro-Electromechanical Systems) devices, multi-line LiDAR systems now capture point clouds numbering in the tens of thousands or even hundreds of thousands per frame. The large data volumes produced by high-frame-rate LiDAR furthercomplicate processing tasks. The generated point cloud data are not only voluminous but also heterogeneous [9], making data processing complex and imposing higher demands on storage, loading, and computational performance. Additionally, 3D point cloud data are typically unordered and lack a fixed grid structure, making its analysis more challenging than traditional 2D image processing, requiring specialized algorithms for effective handling [10]. Numerous researchers worldwide have proposed various methods for point cloud processing, including iterative algorithms, region growing, model segmentation, and deep learning techniques (such as convolutional neural networks and point-based networks), each tailored to specific data types and application scenarios. For instance, [PERSON] et al. [11] proposed a robust dynamic target extraction method, although its applicability remains limited. [PERSON] et al. [12] introduced a real-time detection method suitable for complex environments, but its performance in high-density point cloud processing still has limitations in terms of accuracy. [PERSON] et al. [13] combined YOLOv5 image detection with K-means clustering to improve the accuracy of 3D point cloud processing, but the approach heavily relies on image detection, requiring substantial computational resources. Deep learning models such as Dynamic Graph Convolutional Neural Networks (DGCNN) and ConvPoint, employed by [PERSON] [14] and [PERSON] [15], effectively address the unstructured nature of point clouds and enhance classification accuracy, but these methods are computationally intensive and demand lengthy training times. For most methods of processing 3D point clouds, significant computational resources are often needed to achieve fast processing speeds, underscoring the need for further research into novel techniques for the rapid processing of 3D point clouds. On the other hand, when processing high-precision 3D point clouds acquired by LiDAR in real-time, efficiency is often improved by reducing the data volume. However, this inevitably leads to some loss of precision. Therefore, ensuring the integrity of the point cloud data while processing it both accurately and efficiently has become a critical research challenge. For example, [PERSON], et al. [16] proposed an efficient point cloud segmentation model that reduces computational cost while improving accuracy. [PERSON] et al. [17] enhanced denoising performance using a method based on PCA and K-means. [PERSON] et al. [18] employed the PointASNL method to improve the accuracy and stability of point cloud processing. [PERSON] et al. [19] enhanced the accuracy of engineering structure monitoring through automated point cloud processing techniques.