ContrastCAD: Contrastive Learning-based Representation Learning for
  Computer-Aided Design Models
Minseop Jung Minseong Kim Jibum Kim
The success of Transformer-based models has encouraged many researchers to
learn CAD models using sequence-based approaches. However, learning CAD models
is still a challenge, because they can be represented as complex shapes with
long construction sequences. Furthermore, the same CAD model can be expressed
using different CAD construction sequences. We propose a novel contrastive
learning-based approach, named ContrastCAD, that effectively captures semantic
information within the construction sequences of the CAD model. ContrastCAD
generates augmented views using dropout techniques without altering the shape
of the CAD model. We also propose a new CAD data augmentation method, called a
Random Replace and Extrude (RRE) method, to enhance the learning performance of
the model when training an imbalanced training CAD dataset. Experimental
results show that the proposed RRE augmentation method significantly enhances
the learning performance of Transformer-based autoencoders, even for complex
CAD models having very long construction sequences. The proposed ContrastCAD
model is shown to be robust to permutation changes of construction sequences
and performs better representation learning by generating representation spaces
where similar CAD models are more closely clustered. Our codes are available at
https://github.com/cm8908/ContrastCAD.