  The semantic segmentation of 3D shapes with a high-density of vertices could
be impractical due to large memory requirements. To make this problem
computationally tractable, we propose a neural-network based approach that
produces 3D augmented views of the 3D shape to solve the whole segmentation as
sub-segmentation problems. 3D augmented views are obtained by projecting
vertices and normals of a 3D shape onto 2D regular grids taken from different
viewpoints around the shape. These 3D views are then processed by a
Convolutional Neural Network to produce a probability distribution function
(pdf) over the set of the semantic classes for each vertex. These pdfs are then
re-projected on the original 3D shape and postprocessed using contextual
information through Conditional Random Fields. We validate our approach using
3D shapes of publicly available datasets and of real objects that are
reconstructed using photogrammetry techniques. We compare our approach against
state-of-the-art alternatives.
