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Abstract

The depth of a point in space can be estimated by observing its image position from two different
viewpoints. The classical approach to stereo vision calculates depth from the two projection
equations which together form a stereocamera model. An unavoidable preparatory work for
this solution is a calibration procedure, i.e., estimating the external (position and orientation)
and internal (focal length, lens distortions etc.) parameters of each camera from a set of points
with known spatial position and their corresponding image positions. This is normally done
by iteratively linearizing the single camera models and reestimating their parameters according
to the error on the known datapoints. The advantage of the classical method is the maximal
usage of prior knowledge about the underlying physical processes and the explicit estimation
of meaningful model parameters such as focal length or camera position in space. However,
the approach neglects the nonlinear nature of the problem such that the results critically depend
on the choice of the initial values for the parameters.
In this study, we approach the depth estimation problem from a different point of view by
applying generic machine learning algorithms to learn the mapping from image coordinates
to spatial position. These algorithms do not require any domain knowledge and are able to
learn nonlinear functions by mapping the inputs into a higher-dimensional space. Compared to
classical calibration, machine learning methods give a direct solution to the depth estimation
problem which means that the values of the stereocamera parameters cannot be extracted from
the learned mapping.
On the poster, we compare the performance of classical camera calibration to that of different
machine learning algorithms such as kernel ridge regression, gaussian processes and
support vector regression. Our results indicate that generic learning approaches can lead to
higher depth accuracies than classical calibration although no domain knowledge is used.