This paper presents a vision-based technique and a system developed for the global reconstruction of three-dimensional (3-D) road surfaces. Using the system, the technique globally reconstructs 3-D road surfaces by estimating the global camera pose using the Adaptive Extended Kalman Filter (AEKF) and integrating it with existing local road surface reconstruction techniques. The AEKF adaptively updates the covariance of uncertainties such that the estimation works well even in environments with varying uncertainties. Numerical results show the efficacy of the proposed technique over the Extended Kalman Filter (EKF)-based technique by 50% in accuracy, and the on-road test has demonstrated the ability of the proposed technique for the real-world global 3-D road surface reconstruction.