Improving Computational Efficiency of 3D Point Cloud Reconstruction from Image Sequences

The Levenberg-Marquardt optimization is normally used in 3D point cloud reconstruction from image sequences which is computationally expensive. This paper presents a two-stage camera pose estimation approach where an initial camera pose is obtained during the first stage and a refinement is performed during the second stage. This approach does not require the use of the Levenberg-Marquardt optimization and LU matrix decomposition for computing the projection matrix, thus providing a more computationally efficient 3D point cloud reconstruction as compared to the existing approaches. The results obtained using real video sequences indicate that the introduced approach generates lower re-projection errors as well as faster 3D point cloud reconstruction.