Research On Binocular Stereo Vision Based 3D Reconstruction Of Scene

Stereo vision is recovering 3d depth information of objects in a scene using image pairs taken from two different views through one camera or two. This method is one of the most important passive methods in stereo vision. There are many applications in different areas, such as Stereo-imaging in Remote sensing, Robot navigation and etc. The final goal of stereo vision is to get 3d model from image pair. So getting the depth of the object surface is the key step in the whole process, the depth information is getting through stereo matching. In general, stereo matching can be classified into the local or global approaches. The result of the global approaches achieves high accuracy, and many researchers pay attention to the global approaches. In global approaches, Graph-cut based algorithm and Belief-propagation based algorithm are prominent in applications. Their accuracy is accepted by the researchers. However, due to the ill-posed nature of stereo matching problem, determination of accurate is still a hard problem.In the paper, we lucubrate two aspects of stereo vision system, that are stereo matching and scene reconstruction, the main research content are as follows:In stereo matching, using an Graph-cut based algorithm, this algorithm construct energy function according to color differences and smoothness constraint between neighbor disparity areas, then construct graph corresponding to the energy function and get the min-cut on graph. That is getting the min-value of the energy function and we get the best disparity map.There are many noises and local error disparity pixels in the disparity map got from basic Graph-cut based stereo matching algorithm above. In order to improve the accuracy of disparity map, we introduce mean-shift filtering technology to the basic stereo matching method, our method considers the segmentation using both color and disparity information simultaneously to improve the initial map achieved by the basic algorithm. In the result, our method is so effective in removing the noises and error pixels improve and improving the accuracy of the boundaries in scene, through using edge detection on local disparity map. We can easily find the effective of the algorithm.In scene reconstruction, we compute the depths of the object after stereo matching and get the 3d model of the scene, through texture wrapping we paste the reference image to the 3d model and improve the reality of the 3d scene.