  We present an out-of-core variational approach for surface reconstruction
from a set of aligned depth maps. Input depth maps are supposed to be
reconstructed from regular photos or/and can be a representation of terrestrial
LIDAR point clouds. Our approach is based on surface reconstruction via total
generalized variation minimization ($TGV$) because of its strong
visibility-based noise-filtering properties and GPU-friendliness. Our main
contribution is an out-of-core OpenCL-accelerated adaptation of this numerical
algorithm which can handle arbitrarily large real-world scenes with scale
diversity.
