mlsanityData to 7 Oct 2026

Self-Supervised Representation Learning for CAD

arXiv 2210.10807 ↗First posted 19 Oct 2022cs.CV cs.GRcited by 22

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Abstract (arXiv)

The design of man-made objects is dominated by computer aided design (CAD) tools. Assisting design with data-driven machine learning methods is hampered by lack of labeled data in CAD's native format; the parametric boundary representation (B-Rep). Several data sets of mechanical parts in B-Rep format have recently been released for machine learning research. However, large scale databases are largely unlabeled, and labeled datasets are small. Additionally, task specific label sets are rare, and costly to annotate. This work proposes to leverage unlabeled CAD geometry on supervised learning tasks. We learn a novel, hybrid implicit/explicit surface representation for B-Rep geometry, and show that this pre-training significantly improves few-shot learning performance and also achieves state-of-the-art performance on several existing B-Rep benchmarks.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 34 of its 47 references that are in the map. First posted on arXiv 19 Oct 2022.

Builds on

34 of 47 references.

References · 34

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22 more
  1. 2018AtlasNet: A Papier-Mach'e Approach to Learning 3D Surface Generationcited by 539
  2. 2014Semi-Supervised Learning with Deep Generative Modelscited by 494
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  10. 2021BRepNet: A topological message passing system for solid modelscited by 92
  11. 2021Computer-Aided Design as Languagecited by 82
  12. 2022SolidGen: An Autoregressive Model for Direct B-rep Synthesiscited by 78
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 6; 2025: 5; 2026: 9.

22 papers.

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  1. 2024BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometrycited by 101
  2. 2024CAD-MLLM: Unifying Multimodality-Conditioned CAD Generation With MLLMcited by 74
  3. 2025HoLa: B-Rep Generation using a Holistic Latent Representationcited by 29
  4. 2024CadVLM: Bridging Language and Vision in the Generation of Parametric CAD Sketchescited by 28
  5. 2025Seek-CAD: A Self-refined Generative Modeling for 3D Parametric CAD Using Local Inference via DeepSeekcited by 22
  6. 2024Geometric Deep Learning for Computer-Aided Design: A Surveycited by 14
  7. 2025BrepGPT: Autoregressive B-rep Generation with Voronoi Half-Patchcited by 12
  8. 2024VIRL: Volume-Informed Representation Learning towards Few-shot Manufacturability Estimationcited by 4
  9. 2026FoV-Net: Rotation-Invariant CAD B-rep Learning via Field-of-View Ray Castingcited by 3
  10. 2026Mamba-CAD: State Space Model For 3D Computer-Aided Design Generative Modelingcited by 3
  11. 2026Masked BRep Autoencoder via Hierarchical Graph Transformercited by 3
  12. 2023Representation Learning for Sequential Volumetric Design Taskscited by 2
10 more
  1. 2025A Survey of AI Methods for Geometry Preparation and Mesh Generation in Engineering Simulationcited by 2
  2. 2026SceMoS: Scene-Aware 3D Human Motion Synthesis by Planning with Geometry-Grounded Tokenscited by 2
  3. 2023P2CADNet: An End-to-End Reconstruction Network for Parametric 3D CAD Model from Point Cloudscited by 1
  4. 2024Self-supervised Graph Neural Network for Mechanical CAD Retrievalcited by 1
  5. 2026BRepCLIP: Contrastive Multimodal Pretraining on BRep Primitives for CAD Understandingcited by 1
  6. 2025Attention to Detail: Fine-Scale Feature Preservation-Oriented Geometric Pre-training for AI-Driven Surrogate Modelingcited by 0
  7. 2026Kolmogorov-Arnold Networks-Based Tolerance-Aware Manufacturability Assessment Integrating Design-for-Manufacturing Principlescited by 0
  8. 2026Brep2Shape: Boundary and Shape Representation Alignment via Self-Supervised Transformerscited by 0
  9. 2026HiFi-BRep: High-Fidelity Latent Representation for Robust B-Rep Generationcited by 0
  10. 2026DepthBenchCAD: When Does Deeper Auditing Yield More Reliable Conclusions?cited by 0