MATLAB Implementation for Stereo Vision Matching

This stereo vision matching program is implemented in MATLAB and employs sophisticated computer vision techniques for accurate 3D scene reconstruction. The core functionality includes stereo correspondence algorithms that utilize block matching methods with Sum of Absolute Differences (SAD) or Sum of Squared Differences (SSD) metrics for pixel-wise matching between left and right stereo images. The implementation incorporates epipolar geometry constraints to reduce search space and improve matching efficiency. Key MATLAB functions utilized include stereoParameters for camera calibration, disparity() for computing depth maps, and reconstructScene() for 3D point cloud generation. The program features adaptive window sizing and sub-pixel interpolation for enhanced precision in disparity calculation. Additional capabilities include noise reduction through Gaussian filtering, occlusion handling using left-right consistency checks, and post-processing with weighted median filtering for disparity map refinement. The architecture supports both local (window-based) and semi-global matching approaches, with configurable parameters for different scene complexities. The user interface provides real-time visualization of disparity maps and 3D reconstructions, along with calibration tools for camera setup optimization. This implementation serves as a comprehensive framework for stereo vision applications in robotics, autonomous navigation, and 3D modeling scenarios.