Research and Application of Preprocessing Techniques Based on 3D Point Cloud Data
This paper investigates preprocessing techniques for 3D point cloud data and proposes an improved adaptive filtering algorithm to enhance the quality and processing efficiency of point cloud data in complex scenes.3D point cloud data is widely used in fields such as autonomous driving,robotic vision,and 3D modeling,but it often suffers from noise and outliers during acquisition.The paper analyzes various data acquisition methods including LiDAR,stereo vision systems,and RGB-D cameras,provides a detailed review of existing point cloud filtering and denoising techniques,and compares methods such as mean filtering,median filtering,statistical filtering,and voxel filtering through experiments.To overcome the limitations of traditional methods under complex noise conditions,this paper introduces an enhanced adaptive filtering algorithm that significantly improves filtering effectiveness and computational efficiency through local noise level estimation,dynamic adjustment of filtering parameters,and edge detection and preservation.Experimental results demonstrate that the proposed algorithm effectively removes noise while preserving edge details of the point cloud data,showcasing superior performance across various experimental scenarios.
3D point cloud datapreprocessingfilteringdenoising
万方数据
维普
