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

In reverse engineering, acquired point cloud data often contains substantial noise that directly impacts subsequent 3D modeling and data analysis. MATLAB, leveraging its powerful matrix operations and comprehensive toolbox ecosystem, serves as an effective platform for point cloud denoising tasks.

The primary objectives of point cloud denoising include outlier removal, surface noise smoothing, and preservation of original geometric features. Common methodologies involve statistical filtering, radius filtering, and Moving Least Squares (MLS) based smoothing techniques. Statistical filtering analyzes distribution characteristics of neighboring points and removes isolated noise by setting appropriate thresholds through functions like pcdenoise() with statistical parameters. Radius filtering operates on local density assessments, eliminating discrete points that fail density criteria using neighborhood search functions such as findNeighborsInRadius(). MLS methods achieve noise smoothing through local surface fitting, implementable via MATLAB's pcregistercorr() or custom MLS scripts for surface reconstruction.

MATLAB's Computer Vision Toolbox and Point Cloud Processing Library provide built-in implementations for these algorithms, eliminating the need for manual development of complex mathematical operations. Visualization tools like pcshow() enable direct comparison of pre- and post-denoising point cloud distributions, facilitating parameter tuning and result optimization through iterative visualization.

For massive point cloud datasets, computational efficiency becomes critical. MATLAB's matrix-oriented operations and parallel computing capabilities (using parfor loops or GPU acceleration with gpuArray()) significantly enhance processing speed, ensuring large-scale point cloud denoising tasks complete within practical timeframes through optimized memory management and distributed computing techniques.