  We propose a novel model for 3D semantic completion from a single depth
image, based on a single encoder and three separate generators used to
reconstruct different geometric and semantic representations of the original
and completed scene, all sharing the same latent space. To transfer information
between the geometric and semantic branches of the network, we introduce paths
between them concatenating features at corresponding network layers. Motivated
by the limited amount of training samples from real scenes, an interesting
attribute of our architecture is the capacity to supplement the existing
dataset by generating a new training dataset with high quality, realistic
scenes that even includes occlusion and real noise. We build the new dataset by
sampling the features directly from latent space which generates a pair of
partial volumetric surface and completed volumetric semantic surface. Moreover,
we utilize multiple discriminators to increase the accuracy and realism of the
reconstructions. We demonstrate the benefits of our approach on standard
benchmarks for the two most common completion tasks: semantic 3D scene
completion and 3D object completion.
