Extracting Boundaries from TXT Point Clouds in MATLAB

To extract boundaries from a TXT point cloud in MATLAB, follow this systematic approach with code implementation details: First, perform data preprocessing to eliminate noise and outliers that may compromise boundary extraction accuracy. This typically involves using statistical outlier removal functions like pcdenoise() or implementing custom filters through functions such as rmoutliers() to clean the point cloud data. Next, apply boundary extraction algorithms to identify perimeter points. The alpha shape algorithm can be implemented using the alphaShape() function, where you specify the alpha radius parameter to control boundary tightness. Alternatively, the convex hull algorithm is available through convhull() or convhulln() functions for 2D/3D point clouds respectively, generating the smallest convex polygon containing all points. After extracting boundary points, refine the results using smoothing techniques. Consider applying moving average filters with smoothdata() or implementing morphological operations for noise reduction. For complex boundaries, you may use boundary simplification algorithms like Douglas-Peucker reduction. Finally, validate boundary accuracy by comparing with ground truth data using distance measurement functions such as pdist2(), or conduct simulation tests with predefined benchmark point clouds. Visualization tools like plot() and pcshow() can help qualitatively assess boundary extraction quality against original data.