Simplified Learning of CAD Features Leveraging a Deep Residual Autoencoder
This addresses the challenge of manually annotating expert knowledge for 3D parts, though it appears incremental as it adapts existing architectures to a new domain.
The paper tackles the problem of insufficient labeled training data for 3D CAD model assessment by developing a deep residual 3D autoencoder based on EfficientNet, enabling transfer learning to reduce the need for labeled data.
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.