A maximum likelihood N-camera stereo algorithm

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