mlsanityData to 7 Oct 2026

A Generative Neural Network Approach for 3D Multi-Criteria Design Generation and Optimization of an Engine Mount for an Unmanned Air Vehicle

arXiv 2311.03414 ↗First posted 6 Nov 2023cs.LG cs.AIcited by 1

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Abstract (arXiv)

One of the most promising developments in computer vision in recent years is the use of generative neural networks for functionality condition-based 3D design reconstruction and generation. Here, neural networks learn dependencies between functionalities and a geometry in a very effective way. For a neural network the functionalities are translated in conditions to a certain geometry. But the more conditions the design generation needs to reflect, the more difficult it is to learn clear dependencies. This leads to a multi criteria design problem due various conditions, which are not considered in the neural network structure so far. In this paper, we address this multi-criteria challenge for a 3D design use case related to an unmanned aerial vehicle (UAV) motor mount. We generate 10,000 abstract 3D designs and subject them all to simulations for three physical disciplines: mechanics, thermodynamics, and aerodynamics. Then, we train a Conditional Variational Autoencoder (CVAE) using the geometry and corresponding multicriteria functional constraints as input. We use our trained CVAE as well as the Marching cubes algorithm to generate meshes for simulation based evaluation. The results are then evaluated with the generated UAV designs. Subsequently, we demonstrate the ability to generate optimized designs under self-defined functionality conditions using the trained neural network.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 28 of its 45 references that are in the map. First posted on arXiv 6 Nov 2023.

Builds on

28 of 45 references.

References · 28

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2013Auto-Encoding Variational Bayescited by 3,980
  3. 2015Multi-view Convolutional Neural Networks for 3D Shape Recognitioncited by 564
  4. 2016Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modelingcited by 558
  5. 2021Conditional Variational Autoencoder with Adversarial Learning for End-to-End Text-to-Speechcited by 555
  6. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 552
  7. 2022GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Imagescited by 446
  8. 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 266
  9. 2018Text2Shape: Generating Shapes from Natural Language by Learning Joint Embeddingscited by 169
  10. 2016Generative and Discriminative Voxel Modeling with Convolutional Neural Networkscited by 128
  11. 20223D Common Corruptions and Data Augmentationcited by 120
  12. 2016Perspective Transformer Nets: Learning Single-View 3D Object Reconstruction without 3D Supervisioncited by 106
16 more
  1. 2022Let us Build Bridges: Understanding and Extending Diffusion Generative Modelscited by 102
  2. 2020Discrete Point Flow Networks for Efficient Point Cloud Generationcited by 87
  3. 2021SetVAE: Learning Hierarchical Composition for Generative Modeling of Set-Structured Datacited by 78
  4. 2019Unpaired Point Cloud Completion on Real Scans using Adversarial Trainingcited by 66
  5. 2022ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Modelcited by 62
  6. 2016VConv-DAE: Deep Volumetric Shape Learning Without Object Labelscited by 50
  7. 20183D-LMNet: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Imagecited by 40
  8. 2020Balancing reconstruction error and Kullback-Leibler divergence in Variational Autoencoderscited by 30
  9. 2021PcDGAN: A Continuous Conditional Diverse Generative Adversarial Network For Inverse Designcited by 30
  10. 2020Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designscited by 19
  11. 20193D Shape Synthesis for Conceptual Design and Optimization Using Variational Autoencoderscited by 18
  12. 2018Towards a Robust Parameterization for Conditioning Facies Models Using Deep Variational Autoencoders and Ensemble Smoothercited by 16
  13. 2021Deep Generative Model for Efficient 3D Airfoil Parameterization and Generationcited by 10
  14. 2021Range-GAN: Range-Constrained Generative Adversarial Network for Conditioned Design Synthesiscited by 5
  15. 2022Neural Volumetric Mesh Generatorcited by 5
  16. 2020Accelerating gradient-based topology optimization design with dual-model neural networkscited by 3

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Citing papers per year: 2025: 1.

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  1. 2025A Continuous-Time Consistency Model for 3D Point Cloud Generationcited by 2