  3D geometry is a very informative cue when interacting with and navigating an
environment. This writing proposes a new approach to 3D reconstruction and
scene understanding, which implicitly learns 3D geometry from depth maps
pairing a deep convolutional neural network architecture with an auto-encoder.
A data set of synthetic depth views and voxelized 3D representations is built
based on ModelNet, a large-scale collection of CAD models, to train networks.
The proposed method offers a significant advantage over current, explicit
reconstruction methods in that it learns key geometric features offline and
makes use of those to predict the most probable reconstruction of an unseen
object. The relatively small network, consisting of roughly 4 million weights,
achieves a 92.9% reconstruction accuracy at a 30x30x30 resolution through the
use of a pre-trained decompression layer. This is roughly 1/4 the weights of
the current leading network. The fast execution time of the model makes it
suitable for real-time applications.
