On Multiple-View Matrix Based 3D Reconstruction from Multiple-View Images

In this paper, we propose a multiple-view matrix based 3D reconstruction algorithm for generating a 3D point cloud model for a scene or an object from several sequence images. The algorithm first extracts a group of SIFT (Scale Invariant Feature Transform) feature points from each image, and divides the points into different groups according to the matching degrees among the points. Secondly, a set of 3D point clouds are reconstructed from the feature points with a calculated a multiple-view matrix. Then, a complete result is generated by merging the point clouds with an incremental algorithm and the estimated camera parameters. Furthermore, our result is optimized by employing a BA (Bundle Adjustment) method. Owing to the introduction of the multiple-view matrix and the group-based SIFT matching, our algorithm has the ability to accurately reconstruct a 3D point cloud model only with several images. The performance of our algorithm is evaluated on a group of benchmark datasets, and is compared to two state-of-the-art methods.