Stereo Matching

In this implementation, we provide a comprehensive stereo matching algorithm that computes disparity maps between left and right stereo views. The core functionality involves taking stereo image pairs as input and producing accurate disparity maps as output. Our implementation utilizes standardized reference images from the Middlebury vision database, which are widely adopted in computer vision research due to their representative nature and reproducibility. The algorithm employs advanced stereo matching techniques including cost computation, cost aggregation, and disparity optimization. We have conducted thorough testing and analysis on these benchmark images to ensure our disparity mapping accurately captures the visual differences between left and right perspectives. The implementation incorporates several optimization methods such as census transform for robust matching and semi-global matching (SGM) for handling textureless regions. Key functions include: - Pre-processing module for image normalization and noise reduction - Matching cost calculation using both absolute differences and gradient-based methods - Disparity refinement through sub-pixel interpolation and consistency checks This disparity mapping solution is suitable not only for computer vision research but also for practical applications including depth perception, autonomous driving systems, and 3D reconstruction projects. The code structure follows modular design principles, allowing easy integration with existing vision pipelines and customization for specific use cases.