Name
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. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1007/978-3-319-10602-1_50 | COMPUTER VISION - ECCV 2014, PT V |
Keywords | Field | DocType |
Bundle adjustment, nonlinear least-squares optimization, robust cost function | Mathematical optimization,Nonlinear system,Bundle adjustment,Computer science,Outlier,Optimization algorithm,Almost surely,Nonlinear least squares optimization | Conference |
Volume | ISSN | Citations |
8693 | 0302-9743 | 15 |
PageRank | References | Authors |
0.61 | 13 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher Zach | 1 | 1457 | 84.01 |