  During the last years, many advances have been made in tasks like3D model
retrieval, 3D model classification, and 3D model segmentation.The typical 3D
representations such as point clouds, voxels, and poly-gon meshes are mostly
suitable for rendering purposes, while their use forcognitive processes
(retrieval, classification, segmentation) is limited dueto their high
redundancy and complexity. We propose a deep learningarchitecture to handle 3D
models as an input. We combine this architec-ture with other standard
architectures like Convolutional Neural Networksand autoencoders for computing
3D model embeddings. Our goal is torepresent a 3D model as a vector with enough
information to substitutethe 3D model for high-level tasks. Since this vector
is a learned repre-sentation which tries to capture the relevant information of
a 3D model,we show that the embedding representation conveys semantic
informationthat helps to deal with the similarity assessment of 3D objects. Our
ex-periments show the benefit of computing the embeddings of a 3D modeldata set
and use them for effective 3D Model Retrieval.
