  We present a stereo-matching method for depth estimation from high-resolution
images using visual hulls as priors, and a memory-efficient technique for the
correlation computation. Our method uses object masks extracted from
supplementary views of the scene to guide the disparity estimation, effectively
reducing the search space for matches. This approach is specifically tailored
to stereo rigs in volumetric capture systems, where an accurate depth plays a
key role in the downstream reconstruction task. To enable training and
regression at high resolutions targeted by recent systems, our approach extends
a sparse correlation computation into a hybrid sparse-dense scheme suitable for
application in leading recurrent network architectures. We evaluate the
performance-efficiency trade-off of our method compared to state-of-the-art
methods, and demonstrate the efficacy of the visual hull guidance. In addition,
we propose a training scheme for a further reduction of memory requirements
during optimization, facilitating training on high-resolution data.
