Adaptive Scale Selection for Hierarchical Stereo

Hierarchical stereo provides an efficient coarse-to-fine mechanism for disparity map estimation. However, common drawbacks of such an approach include the loss of high frequency structures not observable at coarse scale levels, as well as the unrecoverable propagation of erroneous disparity estimates through the scale space. This paper presents an adaptive scale selection mechanism to determine a suitable resolution level from which to begin the hierarchical depth estimation process for each pixel. The proposed scale selection mechanism allows us to robustly implement variable cost aggregation in order to reduce the variability of the photo-consistency measure across scale space. We also incorporate a weighted shiftable window mechanism to enable error correction during coarse-to-fine depth refinement. Experiments illustrate the effectiveness of our approach in terms of disparity accuracy, while attaining a computational efficiency compromise between full resolution and hierarchical disparity map estimation.