Reconstruction of Simulated Cubes with MATLAB

In the field of computer vision, 3D reconstruction represents a critical experimental application. Among various approaches, reconstructing simulated cubes using MATLAB is a widely adopted method. This technique employs computer algorithms to transform 2D images into 3D models through a multi-stage pipeline. Key processing stages include image preprocessing, point cloud reconstruction, triangular mesh generation, and surface reconstruction. Image preprocessing involves operations like noise reduction and contrast enhancement using functions such as imfilter() or medfilt2() to optimize raw image quality. Point cloud reconstruction converts 2D pixel coordinates into 3D point clouds through algorithms like stereoscopic vision matching or structure-from-motion techniques, often implemented via vision.PointTracker or vision.StructureFromMotion in MATLAB. Triangular mesh generation transforms point clouds into triangular facets using Delaunay triangulation algorithms (e.g., delaunayTriangulation()) to create wireframe models. Finally, surface reconstruction generates smoothed surface models from triangular meshes through interpolation methods like griddata() or surface fitting functions. Implementing simulated cube reconstruction with MATLAB provides deep insights into 3D reconstruction principles and workflows. This methodology finds practical applications in medical imaging analysis, industrial inspection systems, and augmented reality development. Mastering this technique holds significant importance for both academic learning and practical implementations in computer vision domains.