This paper proposes a voting-based model that predicts depth at weakly-structured image areas from the depth
that is extracted using a feature-based stereo method. We provide results, on both real and artificial scenes,
that show the accuracy and robustness of our approach. Moreover, we compare our method to different dense
stereo algorithms to investigate the effect of texture on performance of the two different approaches. The
results confirm the expectation that dense stereo methods are suited better for textured image areas and our
method for weakly-textured image areas.