IIIT Hyderabad Publications |
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Fast Optimization Solvers for Bundle AdjustmentAuthor: Siddhant Katyan Date: 2021-02-27 Report no: IIIT/TH/2021/30 Advisor:Pawan Kumar AbstractRecently, a lot of the standard algorithms in machine learning and computer vision need to be redesigned for handling large scale problems. In this thesis, we explore one such fundamental algorithm, which is inherent in almost every multi-view reconstruction system pipeline in computer vision. Recently, bundle adjustment has been the key component in Structure-from-Motion (SfM) problem. Bundle adjustment (BA) jointly optimizes the camera parameters and feature points parameters according to an objective function of reprojection errors. This joint optimization of camera parameters and feature points paramters for multi-view reconstruction formulate the BA problem as a non-linear least squares problem which is solved by some variant of the traditional Levenberg-Marquardt (LM) algorithm [44]. Most of the computation in LM goes into repeatedly solving the normal equations that arise as a result of linearizing the objective function. Some widely used methods like Cholesky Decomposition and Preconditioned Conjugate Gradients [25], to solve these normal equations still face some challenges in robustness, accuracy, and efficiency. In this thesis, we propose a deflated algebraic two-grid method has been used as a preconditioner for Generalized Minimal Residual Method (GMRES) [58], for solving these normal equations arising in the Bundle Adjustment process. We show that the proposed method is several times faster than iterative schur and jacobi schur [2] which are considered as state-of-the-art methods to solve the Bundle Adjustment problem. We verify the competence of our algorithm by benchmarking it against the stateof-the-art methods on publicly available Bundle Adjustment in the Large (BAL) dataset [3]. Full thesis: pdf Centre for Security, Theory and Algorithms |
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