Research on 3D reconstruction algorithm based on improved SFM

Aiming at the sparse problem of object point cloud based on structure from motion method, a 3D reconstruction method using different matching data is proposed. The matching points are calculated by contrast context histogram(CCH) algorithm. The M-estimation sampling consensus(MSAC) algorithm is used to calculate the fundamental matrix, the translation and rotation matrix are decomposed from fundamental matrix. The image projection matrix is obtained combining the camera internal parameters. KLT algorithm is used to update the matching data, and the point cloud is generated by triangulation principle. This method makes use of the advantage of high accuracy of CCH algorithm to make the calculation results of the basic matrix converge. Using KLT algorithm to realize the matching by displacement instead of description vector, it makes up for the deficiency of matching data in low frequency region. The experimental results show that the proposed algorithm is effective and feasible, and the reconstructed point cloud has advantages in comparison with existing algorithms, it can be used for building 3D model of objects in the real scene.