Calibration and rectification of vertically aligned binocular omnistereo vision systems

Abstract

Omnidirectional stereo vision systems have been widely used as primary vision sensors in intelligent robot 3D measurement tasks, which require stereo calibration and rectification. Current stereo calibration and rectification methods suffer from complex calculations or a lack of accuracy. This paper establishes a simple and effective equivalency between an omnidirectional stereo vision system and a perspective vision system by studying stereo calibration and rectification methods. First, we improved the stereo calibration method. By applying the essential matrix, the complicated calibration process of the original method is simplified. By using a manual extraction method to extract corner points, noise error is eliminated and high precision is ensured. Second, we propose a new rectification method. By using the proposed simple rectification model and calibration data, the baseline length and an accurate column-aligned image pair are easily obtained, which reduces the computation time. The proposed stereo calibration and rectification method can simply and effectively obtain two key parameters of the triangulation formula for 3D measurement tasks: baseline length and parallax. Using real data captured by equipment, we performed experiments covering all the necessary stages to obtain a high-performance omnidirectional stereo vision system. Statistical analyses of the experimental results demonstrate the effectiveness of the proposed method.

Three‐dimensional face modeling technology based on 5G virtual reality binocular stereo vision

“…For example, Zhifeng et al have developed a VR combination tool called “Virtual Design,” which can make images display with sound in real time 20,21 . Using a single picture as a texture, the human head is driven by lines, synthesizing various natural expressions of a real face simultaneously 22,23 . The basic principle is to predefine a human head grid model with key feature points, then match this grid to the neutral expression of a specific face in the image, and then modify the key feature points of this grid to drive the corresponding area of the image to deform and synthesize people facial expression 13,24 …”