ContrastCAD: Contrastive Learning-based Representation Learning for Computer-Aided Design Models
This work addresses the problem of representation learning for CAD models in computer-aided design, offering incremental improvements through novel augmentation techniques.
The authors tackled the challenge of learning CAD models from long and varied construction sequences by proposing ContrastCAD, a contrastive learning approach that uses dropout and a new Random Replace and Extrude augmentation method, resulting in enhanced performance for Transformer-based autoencoders on imbalanced datasets and robust representation learning with closer clustering of similar models.
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.