We propose an efficient parallel processing approach designed to synchronize multiple cameras in real-time, optimizing communication between cameras and computers for high-precision infrared (IR) marker tracking. Our method significantly reduces inter-camera latency, ensuring that all sensors capture the same motion events at nearly identical timestamps. In the calibration phase, we introduce a refined technique for obtaining initial estimates of internal parameters and adjusting external positions, followed by a nonlinear optimization process to precisely determine the camera’s pose, specifically its extrinsic parameter matrix. In the reconstruction phase, the 3D positions of each infrared marker are calculated with exceptional accuracy, achieving a reprojection error of less than 1 pixel. Optical motion capture systems can maintain real-time performance through this parallel processing and synchronization framework while achieving robust multi-camera calibration and 3D reconstruction. Our results demonstrate that even with multiple IR cameras operating simultaneously, precise marker localization is feasible when camera synchronization is rigorously enforced. The proposed parallel processing method and algorithm improve performance in future optical motion capture applications. In conclusion, we present parallel processing techniques and algorithms that significantly enhance multi-camera systems’ calibration and data processing capabilities, essential for achieving high-precision motion capture and 3D reconstruction.