Summary of Contrastcad: Contrastive Learning-based Representation Learning For Computer-aided Design Models, by Minseop Jung et al.
ContrastCAD: Contrastive Learning-based Representation Learning for Computer-Aided Design Models
by Minseop Jung, Minseong Kim, Jibum Kim
First submitted to arxiv on: 2 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach, ContrastCAD, for learning CAD models using sequence-based methods. While Transformer-based models have been successful in various tasks, learning CAD models remains challenging due to their complex shapes and varied construction sequences. The authors introduce a contrastive learning-based method that captures semantic information within the construction sequences of the CAD model. To enhance learning performance, they also propose a new data augmentation technique, Random Replace and Extrude (RRE), which is particularly effective for imbalanced training datasets. Experimental results demonstrate that RRE significantly improves the performance of Transformer-based autoencoders, even for complex CAD models with long construction sequences. The proposed ContrastCAD model shows robustness to permutation changes in construction sequences and generates representation spaces where similar CAD models are closely clustered. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to learn 3D shapes, called CAD (Computer-Aided Design) models. These models can be complex and have many different ways of being represented. The authors created a new approach called ContrastCAD that helps computers learn these shapes by looking at how the shape is built, step by step. They also came up with a new way to make more data for training the computer, which makes it perform better. The results show that this new approach works well even for very complex shapes and can group similar shapes together in a meaningful way. |
Keywords
» Artificial intelligence » Data augmentation » Transformer
