Denoising Program for Processing Large-Scale Point Cloud Data in Reverse Engineering

This project involves processing large-scale point cloud datasets, for which we developed a MATLAB-based denoising program. The program effectively removes noise from point cloud data through advanced filtering algorithms, significantly improving data quality. In reverse engineering applications, the quality of point cloud data critically impacts final results. Therefore, we prioritize robust point cloud processing to ensure accuracy and reliability of outcomes. Our implementation includes statistical outlier removal methods and radius-based filtering techniques that automatically detect and eliminate anomalous points. Beyond the MATLAB denoising program, we employ additional tools and algorithms for comprehensive point cloud processing, including iterative closest point (ICP) algorithms for point cloud registration and surface reconstruction methods for 3D model generation. These processing stages collectively produce more precise point cloud data, providing stronger support for reverse engineering workflows. The code structure incorporates modular functions for easy integration with other point cloud processing pipelines, featuring customizable parameters for different noise types and data characteristics.