  Patch-based stereo is nowadays a commonly used image-based technique for
dense 3D reconstruction in large scale multi-view applications. The typical
steps of such a pipeline can be summarized in stereo pair selection, depth map
computation, depth map refinement and, finally, fusion in order to generate a
complete and accurate representation of the scene in 3D. In this study, we aim
to support the standard dense 3D reconstruction of scenes as implemented in the
open source library OpenMVS by using semantic priors. To this end, during the
depth map fusion step, along with the depth consistency check between depth
maps of neighbouring views referring to the same part of the 3D scene, we
impose extra semantic constraints in order to remove possible errors and
selectively obtain segmented point clouds per label, boosting automation
towards this direction. I n order to reassure semantic coherence between
neighbouring views, additional semantic criterions can be considered, aiming to
elim inate mismatches of pixels belonging in different classes.
