Computing the Fundamental Matrix for Stereo Vision

When performing 3D reconstruction and coordinate point measurement, it is essential to first compute the fundamental matrix for stereo vision. This fundamental matrix is derived through calibration of both left and right cameras and serves as the mathematical foundation for converting 2D image points into 3D coordinates. In 3D reconstruction workflows, this matrix enables the calculation of camera extrinsic parameters (position and orientation) and intrinsic parameters (focal length, principal point), which are crucial for triangulating 3D points from corresponding image pairs. The fundamental matrix is typically computed using feature matching algorithms (like SIFT or ORB) followed by robust estimation methods such as RANSAC to handle outliers. Additionally, the fundamental matrix facilitates accurate coordinate point measurement by establishing epipolar geometry constraints between stereo images, allowing for precise positional information extraction. Therefore, computing the fundamental matrix represents a critical step in any stereo vision computational pipeline.