Camera Calibration And 3D Reconstruction Based On Binocular Stereo Vision

Three-dimensional reconstruction is a critical classic problem in the field of computer vision , but also the final step of a computer simulating visual function .The non-linear distortion of a two-dimensional image , internal parameter calibration and external parameter calibration are the core technology of three-dimensional reconstruction.Firstly, studing the camera calibration technique in the field of the enhanced real system, give full consideration to the lens radial distortion and tangential distortion effects and the solving method . At first ,calculating the various internal and external camera parameters based on the online model by using perspective projection method , and then introducing distortion based on the non-linear model, solving the distortion coefficient, the final step, optimizing some parameters in order to achieve the initial value to make the back-projection error to a minimum. The algorithm improves the computation efficiency.Under the conditions of lower required equipments,It can get the higher accuracy of calibration results. The experimental results show that the algorithm makes the back-projection error smaller than the perspective projection matrix method's.The calibration results are equal to the Tsai two-step method calibration results, but it can simplify the complexity of nonlinear optimization searching and the complexity of the calibration device. It can meet the requirments of augmented reality system and other computer vision systems.After calibration, introducing the concrete steps and implementation process for three-dimensional reconstruction. Firstly, according to the feature points ,matching principle based on the epipolar constraint to find the corresponding feature points of different two-dimensional images , and then establishing the correspondence between image pairs, using the basic principle of binocular stereo vision, based on the linear triangle method for the reconstruction of three-dimensional shape of the surrounding landscape and location by restoring the scene depth information. The experimental evidence indicates that this reconstruction process is simple and can achieve a higher reconstruction accuracy.