X, Y, Z Coordinates of 3D Point Clouds

3D point cloud data consists of X, Y, Z coordinates from numerous discrete points, forming a digital representation of 3D object surfaces. Specific algorithms can reconstruct complete 3D images from these coordinate datasets. In implementation, point clouds are typically stored as N×3 matrices where each row contains (x,y,z) coordinates, enabling efficient processing through array operations.

The triangular mesh method is a common point cloud reconstruction technique. Its core algorithm connects three adjacent points to form triangular facets, ultimately creating a mesh that covers the entire object surface. This method effectively preserves geometric features while generating lightweight 3D models. Code implementation often involves Delaunay triangulation or ball-pivoting algorithms, where key functions like `delaunayTriangulation()` in MATLAB or `pcl::GreedyProjectionTriangulation` in PCL library automate mesh generation from point coordinates.

Coordinate regularization addresses common uneven distribution issues in point cloud data. Through normalization or grid processing, this technique makes data more suitable for subsequent processing. Implementation typically involves `MinMaxScaler` for normalization or voxel grid downsampling using `pcl::VoxelGrid` filter, significantly improving reconstructed model smoothness and accuracy by standardizing coordinate ranges.

When combining both methods, standard practice involves preprocessing raw coordinates with regularization first, then applying triangular mesh algorithms to generate topologically correct watertight models. This pipeline ultimately achieves high-fidelity 3D reconstruction, where coordinate preprocessing ensures optimal point distribution before mesh construction algorithms like Poisson reconstruction or advancing front methods create seamless surfaces.