Explore MATLAB source code curated for "点云数据" with clean implementations, documentation, and examples.
MATLAB denoising application for handling extensive point cloud datasets in reverse engineering, implementing noise reduction algorithms and data quality enhancement techniques
This program implements algorithms for extracting feature points and detecting boundaries in 3D scattered point clouds
This program primarily performs 3D modeling of point cloud data using MATLAB, providing practical value for three-dimensional reconstruction tasks with MATLAB implementations.
Comprehensive LiDAR point cloud data handling with reading, filtering, and classification capabilities, supporting multiple file formats and advanced processing algorithms
This program converts airborne LiDAR point cloud data into DSM depth images to facilitate subsequent threshold segmentation operations. The implementation involves point cloud interpolation, grid projection, and height value normalization algorithms.
A method for computing normal vectors of 3D point cloud data, successfully implemented in facial modeling applications. Provides excellent performance with accurate normal vector extraction, but requires non-overlapping point clouds to avoid critical errors. Implementation typically involves neighborhood analysis and PCA-based surface fitting.
A point cloud data registration algorithm suitable for advanced 3D modeling processing, featuring excellent registration performance with implementation details for coordinate transformation and surface alignment.