Stereo Vision Image Pair Matching for Disparity Map Acquisition

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Performing stereo matching on binocular vision image pairs to obtain disparity maps, laying the foundation for subsequent 3D reconstruction through depth estimation algorithms.

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In this paper, we need to perform stereo matching on binocular vision image pairs to acquire disparity maps. This step is critically important as it provides the fundamental data for subsequent 3D reconstruction work. The process typically involves implementing stereo correspondence algorithms such as Block Matching or Semi-Global Block Matching (SGBM) to compute horizontal displacement between corresponding pixels. Through the disparity map, we can determine object depth and distance using the triangulation principle, enabling the creation of more realistic and accurate 3D models. Key implementation considerations include handling occlusion regions, optimizing matching cost functions, and applying post-processing techniques like disparity refinement. Therefore, ensuring proper execution of stereo image matching is essential for reliable depth estimation.

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