  Semantic scene completion is the task of predicting a complete 3D
representation of volumetric occupancy with corresponding semantic labels for a
scene from a single point of view. Previous works on Semantic Scene Completion
from RGB-D data used either only depth or depth with colour by projecting the
2D image into the 3D volume resulting in a sparse data representation. In this
work, we present a new strategy to encode colour information in 3D space using
edge detection and flipped truncated signed distance. We also present EdgeNet,
a new end-to-end neural network architecture capable of handling features
generated from the fusion of depth and edge information. Experimental results
show improvement of 6.9% over the state-of-the-art result on real data, for
end-to-end approaches.
