Abstract:This paper studies the preprocessing techniques for three-dimensional point cloud data and proposes an improved adaptive filtering algorithm aimed at enhancing the quality and processing efficiency of point cloud data in complex scenes. Three-dimensional point cloud data, as an important tool for describing the surface shape and spatial position of objects, is widely used in fields such as autonomous driving, robotic vision, and 3D modeling. However, point cloud data often suffer from noise and outliers during the collection process, which poses challenges for subsequent processing and analysis. This paper first analyzes the generation methods and basic characteristics of three-dimensional point cloud data, exploring the features of various data acquisition methods such as LiDAR (Laser Imaging Detection and Ranging), stereo vision systems, and RGB-D cameras. It then provides a detailed introduction to existing point cloud filtering and denoising techniques. Finally, the paper compares and analyzes common methods such as mean filtering, median filtering, statistical filtering, and voxel filtering through experiments. To overcome the limitations of traditional filtering methods under complex noise conditions, this paper proposes an improved adaptive filtering algorithm that significantly enhances filtering effectiveness and computational efficiency through local noise level estimation, dynamic adjustment of filtering parameters, and edge detection and preservation. The research results indicate that the proposed adaptive filtering algorithm effectively removes noise while preserving the edge details of point cloud data and demonstrates excellent performance in various experimental scenarios. Comparative analysis of experimental data shows that this algorithm has high practical utility in point cloud preprocessing, laying a foundation for further processing and application of three-dimensional point cloud data.