  In the domain of computer vision, deep residual neural networks like
EfficientNet have set new standards in terms of robustness and accuracy. One
key problem underlying the training of deep neural networks is the immanent
lack of a sufficient amount of training data. The problem worsens especially if
labels cannot be generated automatically, but have to be annotated manually.
This challenge occurs for instance if expert knowledge related to 3D parts
should be externalized based on example models. One way to reduce the necessary
amount of labeled data may be the use of autoencoders, which can be learned in
an unsupervised fashion without labeled data. In this work, we present a deep
residual 3D autoencoder based on the EfficientNet architecture, intended for
transfer learning tasks related to 3D CAD model assessment. For this purpose,
we adopted EfficientNet to 3D problems like voxel models derived from a STEP
file. Striving to reduce the amount of labeled 3D data required, the networks
encoder can be utilized for transfer training.
