Abstract
This paper extends results of a maximum likelihood two-frame
stereo algorithm to the case of N cameras. The N-camera stereo algorithm
determines the “best” set of correspondences between a given
pair of cameras, referred to as the principal cameras. Knowledge of the
relative positions of the cameras allows the 3D point hypothesized by an
assumed correspondence of two features in the principal pair to be
projected onto the image plane of the remaining N-2 cameras. These N-2
points are then used to verify proposed matches. Not only does the
algorithm explicitly model occlusion between features of the principal
pair, but the possibility of occlusions in the N-2 additional views is
also modelled. The benefits and importance of this are experimentally
verified. Like other multi-frame stereo algorithms, the computational
and memory costs of this approach increase linearly with each additional
view. Experimental results are shown for two outdoor scenes. It is
clearly demonstrated that the number of correspondence errors is
significantly reduced as the number of views/cameras is increased
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