3d Reconstruction Algorithm

Computer vision is a comprehensive discipline whose researches relate to image processing, image comprehension, pattern recognition, computer graphics, signal processing, mathematics and biological physics, etc. The research object of computer vision is to make computers have the ability of understanding 3D environmental information from 2D images or views. Because the research achievements can be applied directly on robot localization and navigation, precise industry measurement, object recognition, visual reality, military affairs and many other fields, the researches on computer vision problem have become one of the most popular research subjects in the world.This paper is focus on 3D reconstruction which is one of most important problem in computer vision. Through 3D reconstruction one can recover 3D information from several images of static object. At last, a complete 3D reconstruction system is realized according to our algorithm. Camera calibration is a very important part in 3D reconstruction, in this paper the discuss on camera calibration is divided into three parts:(l)scene structure information based camera calibration;(2)camera initiative information(pure rotation) based camera self-calibration;(3)camera self-calibration which is independent of scene structure information and camera initiative information. According to above three kinds of circumstances, the realization of camera calibration of fix intrinsic parameter model and changeable intrinsic parameter model is discussed respectively. At last, 3D reconstruction is completed based on the calibration data. Real image and synthetic data experiment results are presented in the end of each chapter.The main research achievements are following:(1) Study how to make use of scene structure information to realize hierarchical 3D reconstruction, and then discuss how camera calibration can berealized under fix intrinsic parameter and changeable intrinsic parameter circumstance. Expatiate the substance of projective reconstruction is solving fundamental matrix, substance of affine reconstruction is solving infinite homography or infinite plane and substance of metric reconstruction is solving absolute conic images. In the solving of absolute conic images, a novel circular point based method is used. The experiments show that the algorithm is feasible.(2) Study how to realize camera self-calibration directly from image sequence under special imaging condition (camera pure rotation). In this term, camera calibration method which is suit to fix intrinsic parameter model and changeable intrinsic parameter model is presented respectively. If we can acquire two sets of images which is taken from different view point, 3D reconstruction can be realized.(3) Studying a more vivid camera self-calibration method, it no longer needs the scene construction information and camera initiative information but only rely on the constraints exit in camera intrinsic parameters. Through the reasonable assumption of intrinsic parameters, we can avoid the general nonlinear and ambiguity in solving Kruppa equation. At last, the camera self-calibration can be simplified to solving a quadratic equation. Under changeable intrinsic parameter circumstance, a linear camera self-calibration method is proposed in this paper.(4) Realize and complete a 3D reconstruction system. In the feature points matching part, this paper put forward a method which uses epipolar constraint and homography constraint synthetically, and significant effect is achieved in actual application.