A Survey of Learning Approaches and Application for 3D Vision

Abstract

Abstract. Three-dimensional (3D) vision extracted from the stereo images or reconstructed from the twodimensional (2D) images is the most effective topic in computer vision and video surveillance. Threedimensional scene is constructed through two stereo images which existing disparity map by Stereo vision. Many methods of Stereo matching which contains median filtering, mean-shift segmentation, guided filter and joint trilateral filters [1] are used in many algorithms to construct the precise disparity map. These methods committed to figure out the image synthesis range in different Stereo matching fields and among these techniques cannot perform perfectly every turn. The paper focuses on 3D vision, introduce the background and process of 3D vision, reviews several classical datasets in the field of 3D vision, based on which the learning approaches and several types of applications of 3D vision were evaluated and analyzed.