  We propose a method to reconstruct, complete and semantically label a 3D
scene from a single input depth image. We improve the accuracy of the regressed
semantic 3D maps by a novel architecture based on adversarial learning. In
particular, we suggest using multiple adversarial loss terms that not only
enforce realistic outputs with respect to the ground truth, but also an
effective embedding of the internal features. This is done by correlating the
latent features of the encoder working on partial 2.5D data with the latent
features extracted from a variational 3D auto-encoder trained to reconstruct
the complete semantic scene. In addition, differently from other approaches
that operate entirely through 3D convolutions, at test time we retain the
original 2.5D structure of the input during downsampling to improve the
effectiveness of the internal representation of our model. We test our approach
on the main benchmark datasets for semantic scene completion to qualitatively
and quantitatively assess the effectiveness of our proposal.
