AI-assisted Single-Image Full-Frame Camera Calibration for Space-Constrained Stereoscopic Systems

Abstract

Camera calibration plays a fundamental role in the wake of the newly emerging image data-driven technologies, where pinpoint accuracy in data is vital to the successful functioning of these systems. Conventional camera calibration algorithms require manual object placement, a process that can be exceptionally time-consuming and labor-intensive, particularly in scenarios where space constraints or delicate equipment are involved. We present an innovative calibration object and AI-aided pre-calibration routine with a specific emphasis on space-restricted environments. The proposed methodology obviates the need for manual multi-image acquisition. This is achieved by fabricating the novel calibration object, which contains 20 checkerboards in different positions and orientations. The precursor routine, assisted by an AI model, isolates and processes the individual checkerboards, which is then used as input for the camera calibration. We report an accuracy of 99.92% for ML-assisted checkerboard separation, with procedure time improved by nearly 64x and overall corrected reprojection error consistently below 0.5 pixels. Incorporating the proposed calibration routine into a 3D vascular imaging stereovision system, we demonstrate a depth resolution of 0.5mm