Introduction to Binocular Vision Algorithm Principles

Binocular vision is a technique that simulates human stereoscopic perception using two cameras to reconstruct 3D space by calculating disparity information from left and right images. The core workflow consists of the following steps:

Camera Calibration Determines intrinsic parameters (focal length, distortion coefficients, etc.) and extrinsic parameters (relative position relationship) of both cameras, ensuring subsequent calculations are based on accurate geometric models. Calibration is typically performed using chessboard patterns through functions like OpenCV's calibrateCamera() which solves camera matrix and distortion coefficients.

Image Rectification Projects left and right images onto the same plane (epipolar rectification) using transformations such as stereoRectify() in OpenCV, aligning matching points along the same horizontal line to simplify stereo matching computations.

Stereo Matching Identifies corresponding pixel points in left and right images (e.g., edges or corners of workpieces). Common algorithmic approaches include: Local Matching: Compares pixel block similarity using sliding windows via methods like SAD (Sum of Absolute Differences) or SSIM (Structural Similarity Index). Global Matching: Optimizes energy functions using algorithms like SGM (Semi-Global Matching), suitable for complex scenes but computationally intensive. Implementation often involves dynamic programming or graph cuts.

Disparity Calculation and Depth Mapping Utilizes triangulation principle to compute depth values based on horizontal displacement (disparity) and camera baseline distance. The fundamental formula is: [Depth = \frac{Baseline Distance × Focal Length}{Disparity}] Larger disparity indicates closer object distance. This can be implemented using OpenCV's StereoBM or StereoSGBM classes for real-time processing.

3D Reconstruction and Dimension Measurement Converts depth maps to point clouds using reprojectImageTo3D() function. Actual dimensions are obtained by fitting geometric features (planes, cylinders) or directly calculating point cloud distances. Critical attention to unit conversion (pixels → millimeters) is required using coordinate transformation matrices.

Extended Applications Optimizing matching algorithms improves accuracy in occluded regions through techniques like left-right consistency checks. Integration with deep learning (e.g., PSMNet architecture) enhances matching performance for complex textures using convolutional neural networks. Industrial applications must consider lighting stability and periodic calibration maintenance for sustained accuracy.

Through this pipeline, binocular systems enable non-contact workpiece dimension measurement, suitable for automated quality inspection or robotic grasping scenarios with typical accuracy ranging from 0.1mm to 1mm depending on configuration.