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• W145691285 abstract "In this work we address robust estimation in the bundle adjustment procedure. Typically, bundle adjustment is not solved via a generic optimization algorithm, but usually cast as a nonlinear least-squares problem instance. In order to handle gross outliers in bundle adjustment the least-squares formulation must be robustified. We investigate several approaches to make least-squares objectives robust while retaining the least-squares nature to use existing efficient solvers. In particular, we highlight a method based on lifting a robust cost function into a higher dimensional representation, and show how the lifted formulation is efficiently implemented in a Gauss-Newton framework. In our experiments the proposed lifting-based approach almost always yields the best (i.e. lowest) objectives." @default.
• W145691285 created "2016-06-24" @default.
• W145691285 creator A5020965719 @default.
• W145691285 date "2014-01-01" @default.
• W145691285 modified "2023-09-26" @default.
• W145691285 title "Robust Bundle Adjustment Revisited" @default.
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• W145691285 doi "https://doi.org/10.1007/978-3-319-10602-1_50" @default.
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