  We address the task of 3D semantic scene completion, i.e. , given a single
depth image, we predict the semantic labels and occupancy of voxels in a 3D
grid representing the scene. In light of the recently introduced generative
adversarial networks (GAN), our goal is to explore the potential of this model
and the efficiency of various important design choices. Our results show that
using conditional GANs outperforms the vanilla GAN setup. We evaluate these
architecture designs on several datasets. Based on our experiments, we
demonstrate that GANs are able to outperform the performance of a baseline 3D
CNN in case of clean annotations, but they suffer from poorly aligned
annotations.
