Obtaining Disparity Maps for Image Pairs in Stereo Matching

In stereo vision systems, disparity map acquisition from image pairs provides critical depth information that can be leveraged for comprehensive 3D reconstruction workflows. Through advanced stereo matching algorithms like Semi-Global Block Matching (SGBM) or graph-cut optimization, we can generate high-precision disparity maps using key OpenCV functions such as cv2.StereoSGBM_create(). These algorithms typically implement cost computation, cost aggregation, disparity selection, and refinement stages to establish pixel correspondences between left and right images. The resulting disparity maps serve as fundamental inputs for generating accurate 3D point clouds through triangulation calculations, enabling applications in robotics navigation, augmented reality, and industrial inspection. Furthermore, disparity data facilitates depth estimation algorithms for object recognition systems and contributes significantly to computer vision research by providing ground truth for depth-aware neural networks like PSMNet.