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Bundle adjustment
Once the structure and motion has been obtained for the whole sequence, it is recommended to refine it through a global minimization step. A maximum likelihood estimation can be obtained through bundle adjustment [12,137]. The goal is to find the projection matrices
and the 3D points
for which the mean squared distances between the observed image points and the reprojected image points
is minimized. For views and points the following criterion should be minimized:

(A1) 
where
is the Euclidean image distance. If the image error is zeromean Gaussian then bundle adjustment is the Maximum Likelihood Estimator. Although it can be expressed very simply, this minimization problem is huge. For a typical sequence of 20 views and 2000 points, a minimization problem in more than 6000 variables has to be solved. A straightforward computation is obviously not feasible. However, the special structure of the problem can be exploited to solve the problem much more efficiently.
The observed points being fixed, a specific residual
is only dependent on the point th point and the th camera view. This results in a sparse structure for the normal equations. Using this structure the points can be eliminated from the equations, yielding a much smaller but denser problem. Views that have features in common are now related. For a long sequence where features tend to only be seen in a few consecutive views, the matrix that has to be solved is still sparse (typically band diagonal). In this vcase it can be very interesting to make use of sparse linear algebra algorithms, e.g. [3].
Before going more into detail on efficiently solving the bundle adjustment, the LevenbergMarquardt minimization is presented. Based one this an efficient method for bundle adjustment will be proposed in Section A.2.
Subsections
Next: LevenbergMarquardt minimization
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Marc Pollefeys
20021122