Stereo Vision 3D Reconstruction Program

This program implements a stereo vision-based 3D reconstruction system that takes matching point pair matrices from left and right cameras as input, automatically computes projection matrices through camera calibration parameters, and outputs 3D point coordinates in projective space. The core objective is to achieve accurate 3D reconstruction using stereo vision technology, obtaining precise and detailed surface information of objects. The program operates through several key computational stages: it first processes the input matching point pairs using epipolar geometry constraints, then calculates the fundamental and essential matrices to establish the spatial relationship between cameras. Through triangulation algorithms (such as linear triangulation or optimal methods), the program converts 2D image coordinates from both views into 3D coordinates in projective space. This transformation enables the acquisition of accurate positional and shape information of objects in three-dimensional space. The implementation typically involves OpenCV functions like cv2.stereoCalibrate() for camera calibration and cv2.triangulatePoints() for 3D point computation, ensuring robust mathematical foundations through singular value decomposition (SVD) for matrix solutions. This program finds widespread applications in computer vision, robotic vision, virtual reality, and augmented reality domains, providing researchers and developers with a powerful tool for spatial understanding and scene reconstruction.