||Automatic Recovery of Camera Positions in Urban Scenes
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|Accurate camera calibration is crucial to the reconstruction of
three-dimensional geometry and the recovery of photometric scene
properties. Calibration involves the determination of intrinsic
parameters (e.g. focal length, principal point, and radial lens
distortion) and extrinsic parameters (orientation and position).
In urban scenes and other environments containing sufficient geometric
structure, it is possible to decouple extrinsic calibration into
rotational and translational components that can be treated separately,
simplifying the registration problem. Here we present such a decoupled
formulation and describe methods for automatically recovering the
positions of a large set of cameras given intrinsic calibration,
relative rotations, and approximate positions.
Our algorithm first estimates the directions of translation (up to an
unknown scale factor) between adjacent camera pairs using point features
but without requiring explicit correspondence between them. This
technique combines the robustness and simplicity of a Hough transform
with the accuracy of Monte Carlo expectation maximization. We then find
a set of distances between the pairs that produces globally-consistent
camera positions. Novel uncertainty formulations and match plausibility
criteria improve reliability and accuracy.
We assess our system's performance using both synthetic data and a large
set of real panoramic imagery. The system produces camera positions
accurate to within 5 centimeters in image networks extending over
hundreds of meters.
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