mlsanityData to 14 Oct 2026

Don't Mesh with Me: Generating Constructive Solid Geometry Instead of Meshes by Fine-Tuning a Code-Generation LLM

arXiv 2411.15279 ↗First posted 22 Nov 2024cs.LG cs.GRcited by 6

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Abstract (arXiv)

While recent advancements in machine learning, such as LLMs, are revolutionizing software development and creative industries, they have had minimal impact on engineers designing mechanical parts, which remains largely a manual process. Existing approaches to generating 3D geometry most commonly use meshes as a 3D representation. While meshes are suitable for assets in video games or animations, they lack sufficient precision and adaptability for mechanical engineering purposes. This paper introduces a novel approach for the generation of 3D geometry that generates surface-based Constructive Solid Geometry (CSG) by leveraging a code-generation LLM. First, we create a dataset of 3D mechanical parts represented as code scripts by converting Boundary Representation geometry (BREP) into CSG-based Python scripts. Second, we create annotations in natural language using GPT-4. The resulting dataset is used to fine-tune a code-generation LLM. The fine-tuned LLM can complete geometries based on positional input and natural language in a plausible way, demonstrating geometric understanding.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 32 of its 51 references that are in the map. First posted on arXiv 22 Nov 2024.

Builds on

32 of 51 references.

References · 32

  1. 2022DreamFusion: Text-to-3D using 2D Diffusioncited by 3,025
  2. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  3. 2024DeepSeek-Coder: When the Large Language Model Meets Programming -- The Rise of Code Intelligencecited by 1,181
  4. 2019Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representationscited by 864
  5. 2021Diffusion Probabilistic Models for 3D Point Cloud Generationcited by 735
  6. 2022Point-E: A System for Generating 3D Point Clouds from Complex Promptscited by 699
  7. 20163D-R2N2: A Unified Approach for Single and Multi-view 3D Object Reconstructioncited by 588
  8. 2016Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modelingcited by 558
  9. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  10. 20213D Shape Generation and Completion through Point-Voxel Diffusioncited by 516
  11. 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 488
  12. 2018ABC: A Big CAD Model Dataset For Geometric Deep Learningcited by 341
20 more
  1. 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 271
  2. 2020PolyGen: An Autoregressive Generative Model of 3D Meshescited by 269
  3. 2023MeshGPT: Generating Triangle Meshes with Decoder-Only Transformerscited by 259
  4. 2021CLIP-Forge: Towards Zero-Shot Text-to-Shape Generationcited by 254
  5. 2019Learning Shape Templates with Structured Implicit Functionscited by 242
  6. 2019BSP-Net: Generating Compact Meshes via Binary Space Partitioningcited by 206
  7. 2017Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputscited by 189
  8. 20193D Point Cloud Generative Adversarial Network Based on Tree Structured Graph Convolutionscited by 173
  9. 2016Generative and Discriminative Voxel Modeling with Convolutional Neural Networkscited by 128
  10. 2022SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebookscited by 125
  11. 2024BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometrycited by 103
  12. 2023Hierarchical Neural Coding for Controllable CAD Model Generationcited by 86
  13. 2020UCSG-Net -- Unsupervised Discovering of Constructive Solid Geometry Treecited by 78
  14. 2021CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assemblycited by 62
  15. 2022ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Modelcited by 62
  16. 2021CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsingcited by 51
  17. 2024Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Promptscited by 23
  18. 2023Experiments on Generative AI-Powered Parametric Modeling and BIM for Architectural Designcited by 3
  19. 2022Simplified Learning of CAD Features Leveraging a Deep Residual Autoencodercited by 2
  20. 2022SimCURL: Simple Contrastive User Representation Learning from Command Sequencescited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 3; 2026: 3.

6 papers.