MATLAB Implementation of Stereo Matching Algorithm with Pyramid-Based Approach

Stereo matching is a fundamental computer vision technique for calculating disparity between corresponding points in two images, widely applied in 3D reconstruction and robot navigation. The pyramid-based stereo matching implementation significantly improves computational efficiency through hierarchical processing.

The core algorithm employs an image pyramid structure, computing disparities from low to high resolution layers. At each level, block matching strategies (such as SAD or SSD) measure similarity, while using the previous layer's disparity results as initial estimates to progressively refine matching accuracy. This hierarchical approach effectively reduces search range and avoids computational burdens from global searches on high-resolution images.

In MATLAB implementation, the process typically begins with Gaussian pyramid construction, followed by these key steps: - Use imPyramid or similar functions to generate multi-level pyramids through downsampling of reference and target images - Apply full search window matching at the top pyramid level using functions like normxcorr2 for correlation-based matching - Guide subsequent layers by using upper-level disparity maps to narrow search ranges, implemented through constrained search windows - Enhance disparity map precision through subpixel interpolation using interp2 or parabolic fitting functions

The method's advantages include reducing time complexity from O(n²) to O(n log n) via pyramid structure, while multi-layer optimization effectively handles occluded regions. Output includes disparity maps and 3D point cloud data, directly usable for depth perception applications. Note that algorithm parameters (pyramid levels, matching window size) require scenario-specific adjustment to balance accuracy and speed, achievable through parameter tuning functions in MATLAB's Computer Vision Toolbox.