MATLAB Implementation for Structured Light System Calibration

Structured light system calibration is a critical step in 3D reconstruction, primarily used to accurately match image coordinates captured by cameras with real-world 3D coordinates. Implementing this process in MATLAB typically involves the following core components: Calibration Board Preparation A high-precision checkerboard or dot pattern calibration board is used, with its physical dimensions precisely measured in advance. The calibration board serves as a reference object with known 3D coordinates for subsequent image feature extraction. In MATLAB implementation, the `detectCheckerboardPoints` function from the Computer Vision Toolbox can automatically identify checkerboard corners with subpixel accuracy. Camera Calibration First, traditional camera calibration (using MATLAB's Camera Calibrator toolbox) is performed to obtain camera intrinsic parameters (focal length, principal point coordinates, distortion coefficients) and extrinsic parameters (rotation matrix, translation vector). The `cameraCalibrator` app provides an interactive interface, while the `estimateCameraParameters` function allows programmatic calibration with automatic distortion correction. Projector Calibration In structured light systems, the projector can be treated as an inverse camera. By projecting specific coding patterns (such as Gray codes or phase-shifting fringes) onto the calibration board and capturing the deformed patterns with the camera, the projector's intrinsic and extrinsic parameters are calculated using the camera calibration results. MATLAB implementations typically use `graycode` pattern generation functions and phase unwrapping algorithms like `unwrap` to decode projected patterns. 3D Coordinate Reconstruction Based on the positions of feature points on the calibration board in the images, combined with camera and projector parameters, 3D coordinates of feature points are calculated using triangulation principles. This process requires solving nonlinear optimization problems, typically using least squares methods (`lsqnonlin`) or bundle adjustment optimization (`bundleAdjustment` function in Computer Vision Toolbox) to refine results. System Verification and Registration Finally, calibration accuracy is evaluated through reprojection error, and affine transformations or ICP (Iterative Closest Point) algorithms (`pcregistericp` function) are used to register the reconstructed 3D point cloud with actual calibration board coordinates, ensuring that the system's output 3D data matches physical dimensions. The `pcshow` function can visualize point cloud registration results. In MATLAB, the Image Processing Toolbox and Computer Vision Toolbox can streamline image processing and calibration workflows, while custom scripts implement projector calibration and 3D reconstruction logic. An optimized calibration system can significantly improve measurement accuracy for structured light 3D scanning applications.