Stereo Matching Algorithm Implementation

As mentioned in the document, we can utilize stereo matching algorithms to obtain disparity map code for left and right views of stereo images. Stereo matching represents a fundamental computer vision algorithm that calculates disparity values by comparing pixel value differences between left and right views, thereby extracting depth information from stereo imagery. The core implementation typically involves several key steps: 1. Preprocessing to normalize image intensities and reduce noise 2. Cost computation using methods like SAD (Sum of Absolute Differences), SSD (Sum of Squared Differences), or census transforms 3. Cost aggregation through window-based filtering to smooth disparity estimates 4. Disparity optimization using techniques such as winner-takes-all or dynamic programming 5. Post-processing for discontinuity preservation and occlusion handling By employing stereo matching algorithms, we can effectively comprehend the spatial structure of stereo images and perform advanced analysis of their features and patterns. Therefore, when processing stereo images, acquiring disparity map code for left and right views becomes crucial for applications like 3D reconstruction, autonomous navigation, and depth-aware image processing. The implementation often utilizes OpenCV functions like StereoBM or StereoSGBM for efficient disparity calculation.