Efficient Visualization Technology for Multi-source LiDAR Point Cloud Data and Generative AI-assisted Applications

Aiming at the problems of data loss, noise interference, and limited precision of traditional processing methods in complex scene LiDAR point cloud data, this study deeply explores the integration path of Generative Artificial Intelligence (GenAI) and LiDAR point cloud data processing. By constructing a multi-source data fusion framework, combining 3D Gaussian grid reconstruction algorithms, and point cloud-RGB image registration and fusion technologies, an efficient visualization platform based on the Unreal Engine is designed and implemented. Actual scene tests verify that the scheme significantly improves data integrity repair, feature extraction accuracy, and visualization efficiency. For example, the data integrity repair rate has increased by 30%, the feature extraction accuracy has improved by 20 percentage points, and the visualization efficiency has been enhanced by 2.5 times, providing innovative and practical technical solutions for fields such as autonomous driving and intelligent transportation.