Research On 3D Reconstruction Technology Based On Stereo Vision

A camera is an extraordinarily useful measuring device which produces not only a realistic picture of the scene, but also provides geometric properties information. At present, 3D reconstruction from images is one of the most active areas in computer vision. Many applications require 3D reconstruction such as robot navigation, object recognition and tracking, architecture, archeology, virtual reality, medical diagnosis, military and etc. Theoretically speaking, image captured by the camera generally produces 2D gray image, it is the function combined by many other factors such as geometrical properties of 3D objects, illumination, surface property of objects, camera parameters and images of the environment. These problems are often non-linear which. How to restore the three-dimension information through the two-dimension images affected by the noise is always difficult in computer vision.Stereo matching, camera calibration and 3D reconstruction are three parts of 3D reconstruction based on stereo vision. Detailed studies had been carried out on these parts. In stereo matching part, firstly subpixel corners were detected as feature points that improve the location accuracy effectively. Through the correlation between the two stereo images, candidate matching sets are obtained. However, due to the image noise, feature extraction algorithm, as well as local similarity of non-stereo matching points, there must be some false matching between the images. Consequently, epipolar geometry was introdued to eliminate the outliers, the robust method called LMedS had been carried out to compute fundamental matrix, and then recovered the epipolar geometry, removed false matching points, obtained the new matching sets. After the above matching steps, high precise matching point sets were obtained which removed some uncertainty and outliers correctly, epipolar geometry is also relatively accurate. At the same time, the final matching points are much rare. So the fundamental matrix was used to obtain some new matching points according to the epipolar geometry, and got more matching points correctly. In this paper, VC++ and OpenCV library that provided by Intel for the devepment of Computer Vision was used, achieved stereo matching between two images.Camera calibration is very important and difficult step in stereo vision. calibration results will have a direct impact on the effect of 3D Reconstruction. Because of experimental conditions, this paper only introduced the traditional calibration methods and self-calibration methods.In the visualization of 3D Reconstruction, this paper finally achieved 3D Reconstruction base on OpenGL.