Stereo matching algorithm

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Stereo matching algorithm

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Stereo matching is a fundamental technique in computer vision used to estimate depth information from a pair of stereo images. The core idea involves finding corresponding pixels between the left and right images to compute the disparity, which represents the horizontal shift between matched points.

One common method for stereo matching is the Sum of Squared Differences (SSD) algorithm. SSD works by comparing small patches around candidate pixels in the left and right images. For each pixel in the left image, a window of pixels is taken, and the SSD is calculated between this window and potential matching windows in the right image. The disparity is then determined by finding the position where the SSD is minimized, indicating the best match.

The SSD approach is straightforward but sensitive to noise and variations in lighting conditions. Despite its simplicity, it serves as a foundational method, often extended with optimizations like window size adjustments or preprocessing steps to improve robustness. More advanced techniques, such as semi-global matching or deep learning-based methods, build upon these principles to achieve higher accuracy in disparity estimation.