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From Krylov Spaces to Power Series: Advancing Large-Scale Bundle Adjustment With and Without Initialization

MCML Authors

Abstract

This dissertation advances large-scale bundle adjustment by exploiting its structure. It introduces multidirectional Krylov solvers, proposes inverse expansion methods using power series, and extends variable projection techniques. These contributions improve scalability, efficiency, and robustness, opening new directions beyond classical approaches.

phdthesis Web25a


Dissertation

TU München. Nov. 2025

Authors

S. Weber

Links

URL

Research Area

 B1 | Computer Vision

BibTeXKey: Web25a

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