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Optimizing CAD Models with Latent Space Manipulation

arXiv 2303.12739 ↗First posted 9 Mar 2023cs.CV cs.AI cs.CEcited by 0

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Abstract (arXiv)

When it comes to the optimization of CAD models in the automation domain, neural networks currently play only a minor role. Optimizing abstract features such as automation capability is challenging, since they can be very difficult to simulate, are too complex for rule-based systems, and also have little to no data available for machine-learning methods. On the other hand, image manipulation methods that can manipulate abstract features in images such as StyleCLIP have seen much success. They rely on the latent space of pretrained generative adversarial networks, and could therefore also make use of the vast amount of unlabeled CAD data. In this paper, we show that such an approach is also suitable for optimizing abstract automation-related features of CAD parts. We achieved this by extending StyleCLIP to work with CAD models in the form of voxel models, which includes using a 3D StyleGAN and a custom classifier. Finally, we demonstrate the ability of our system for the optimiziation of automation-related features by optimizing the grabability of various CAD models. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer review under the responsibility of the scientific committee of the 33rd CIRP Design Conference.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 23 references that are in the map. First posted on arXiv 9 Mar 2023.

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18 of 23 references.

References · 18

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  4. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  5. 2020Training Generative Adversarial Networks with Limited Datacited by 1,184
  6. 2021StyleCLIP: Text-Driven Manipulation of StyleGAN Imagerycited by 990
  7. 2020Encoding in Style: a StyleGAN Encoder for Image-to-Image Translationcited by 727
  8. 2019Interpreting the Latent Space of GANs for Semantic Face Editingcited by 691
  9. 2018ABC: A Big CAD Model Dataset For Geometric Deep Learningcited by 339
  10. 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 266
  11. 2015Learning to Compare Image Patches via Convolutional Neural Networkscited by 109
  12. 2019Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesiscited by 106
6 more
  1. 2021Only a Matter of Style: Age Transformation Using a Style-Based Regression Modelcited by 92
  2. 2021Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Datacited by 53
  3. 20213D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Imagescited by 32
  4. 2022Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methodscited by 2
  5. 2022Towards automated Capability Assessment leveraging Deep Learningcited by 2
  6. 2022Simplified Learning of CAD Features Leveraging a Deep Residual Autoencodercited by 2

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