Geometry-aware super-resolution fusion calibration for binocular structured light 3D reconstruction

Accurate binocular camera calibration is essential for reliable three-dimensional reconstruction in scientific imaging, industrial inspection, optical measurement and other applications that depend on precise geometric information. However, feature-point localization remains vulnerable to noise, blur and distortion, which limits calibration accuracy, weakens robustness and reduces performance in uncontrolled real-world imaging conditions across practical systems. Here we present a geometric-fusion calibration framework that combines saddle-point preservation, unbiased cell centroids, and binocular epipolar constraints to improve the stability and reliability of global feature points in structured-light reconstruction. Experiments show that the method improves corner localization under image degradation, reduces reprojection error by 17%, and produces more accurate point clouds in real-world reconstruction tasks. These results support more reliable precision three-dimensional imaging for medical endoscopy, immersive display systems, embodied perception platforms and other compact active sensing technologies that require accurate geometric reconstruction in challenging environments. Accurate camera calibration is critical for high-quality 3D reconstruction, but its performance is often degraded by noise, blur and distortion in real scenes. This work presents a geometric-fusion calibration method that improves featurepoint accuracy, lowers reprojection error by 17%, and enables more accurate 3D reconstruction for practical applications.