Abstract
Bundle adjustment based on collinearity is the most widely used optimization method within image based scene reconstruction. It incorporates observed image coordinates, exterior and intrinsic camera parameters as well as object space coordinates of the observed points. The latter dominate the resulting nonlinear system, in terms of the number of unknowns which need to be estimated. In order to reduce the size of the problem regarding memory footprint and computational effort, several approaches have been developed to make the process more efficient, e.g. by exploitation of sparsity or hierarchical subdivision. Some recent developments express the bundle problem through epipolar geometry and scale consistency constraints which are free of object space coordinates. These approaches are usually referred to as structureless bundle adjustment. The number of unknowns in the resulting system is drastically reduced. However, most work in this field is focused on optimization towards speed and considers calibrated cameras, only. We present our work on structureless bundle adjustment, focusing on precision issues as camera calibration and residual weighting. We further investigate accumulation of constraint residuals as an approach to decrease the number of rows of the Jacobian matrix.
Highlights
For many decades bundle adjustment (BA) has been the method of choice to accurately estimate the relations between points in object space and their projections to images of a scene
In this paper we investigate an approach to structureless bundle adjustment which is closely related to GEA - global epipolar adjustment (Rodriguez et al, 2011a, 2011b), and partially to iLBA - incremental light bundle adjustment (Indelman, 2012, Indelman et al, 2012) and relative bundle adjustment (Steffen et al, 2010)
The remaining triplets are oriented by the estimation of camera poses based on the pairwise orientations
Summary
For many decades bundle adjustment (BA) has been the method of choice to accurately estimate the relations between points in object space and their projections to images of a scene. The collinearity equation, as the underlying mathematical model, incorporates observed image coordinates, exterior and interior camera parameters as well as object space coordinates of the observed points. The latter heavily dominate the resulting system of equations, regarding the number of unknowns to be solved. Some recent developments describe the bundle problem through epipolar and scale consistency constraints, which are free of object space coordinates These approaches are often referred to as structureless bundle adjustment. A structureless approach reduces the amount of unknowns to 0.2%, compared to classical BA This is effective for use with images of higher resolution, which produce a high number of object points.
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