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Error Minimization in 3-Dimensional Model Reconstruction Using Sparse Bundle Adjustment and the Levenberg-Marquardt Algorithm on Stereo Camera Pairs

In computer vision and image processing, it is often desirable to reconstruct the 3D geometry of a scene using 2D images. Bundle adjustment is a common optimization algorithm often used as the final step of this type of 3D model reconstruction to simultaneously refine scene and camera parameters using the Levenberg-Marquardt non-linear least squares minimization algorithm to reduce reprojection error. sba, a C/C++ implementation of sparse bundle adjustment, was developed by Lourakis and Argyros for computational effectiveness by taking advantage of the sparseness of matrices involved in the LevenbergMarquardt computation. We utilize sba to implement bundle adustment for a stereo camera model, in which pairs of cameras are described by both individual and joint parameters. However, sba is not designed for stereo models. This paper highlights the development of a stereo camera model in order in order to interface data collected by stereo camera pairs with sba to obtain optimal results for 3D reconstruction.