mlsanityData to 7 Oct 2026

DTGBrepGen: A Novel B-rep Generative Model through Decoupling Topology and Geometry

arXiv 2503.13110 ↗First posted 17 Mar 2025cs.CVcited by 20

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Abstract (arXiv)

Boundary representation (B-rep) of geometric models is a fundamental format in Computer-Aided Design (CAD). However, automatically generating valid and high-quality B-rep models remains challenging due to the complex interdependence between the topology and geometry of the models. Existing methods tend to prioritize geometric representation while giving insufficient attention to topological constraints, making it difficult to maintain structural validity and geometric accuracy. In this paper, we propose DTGBrepGen, a novel topology-geometry decoupled framework for B-rep generation that explicitly addresses both aspects. Our approach first generates valid topological structures through a two-stage process that independently models edge-face and edge-vertex adjacency relationships. Subsequently, we employ Transformer-based diffusion models for sequential geometry generation, progressively generating vertex coordinates, followed by edge geometries and face geometries which are represented as B-splines. Extensive experiments on diverse CAD datasets show that DTGBrepGen significantly outperforms existing methods in both topological validity and geometric accuracy, achieving higher validity rates and producing more diverse and realistic B-reps. Our code is publicly available at https://github.com/jinli99/DTGBrepGen.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 27 of its 39 references that are in the map. First posted on arXiv 17 Mar 2025.

Builds on

27 of 39 references.

References · 27

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  4. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  5. 2022Classifier-Free Diffusion Guidancecited by 5,717
  6. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  7. 2015Pointer Networkscited by 882
  8. 2022DiGress: Discrete Denoising diffusion for graph generationcited by 503
  9. 2018ABC: A Big CAD Model Dataset For Geometric Deep Learningcited by 339
  10. 2020PolyGen: An Autoregressive Generative Model of 3D Meshescited by 267
  11. 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 266
  12. 2022Neural Wavelet-domain Diffusion for 3D Shape Generationcited by 132
15 more
  1. 2022SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebookscited by 121
  2. 2024BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometrycited by 101
  3. 2019Write, Execute, Assess: Program Synthesis with a REPLcited by 92
  4. 2023Hierarchical Neural Coding for Controllable CAD Model Generationcited by 84
  5. 2022SolidGen: An Autoregressive Model for Direct B-rep Synthesiscited by 78
  6. 2021JoinABLe: Learning Bottom-up Assembly of Parametric CAD Jointscited by 77
  7. 2019Learning to Infer and Execute 3D Shape Programscited by 68
  8. 2020PIE-NET: Parametric Inference of Point Cloud Edgescited by 67
  9. 2021Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinderscited by 59
  10. 2023SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sketch-Extrude Operationscited by 46
  11. 2022Reconstructing editable prismatic CAD from rounded voxel modelscited by 41
  12. 2020Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequencescited by 36
  13. 2023Point2CAD: Reverse Engineering CAD Models from 3D Point Cloudscited by 33
  14. 2023Neurosymbolic Models for Computer Graphicscited by 27
  15. 2022ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generationcited by 17

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 16.

20 papers.

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  1. 2025AutoBrep: Autoregressive B-Rep Generation with Unified Topology and Geometrycited by 16
  2. 2025BrepGPT: Autoregressive B-rep Generation with Voronoi Half-Patchcited by 12
  3. 2026Towards High-Fidelity CAD Generation via LLM-Driven Program Generation and Text-Based B-Rep Primitive Groundingcited by 6
  4. 2026FoV-Net: Rotation-Invariant CAD B-rep Learning via Field-of-View Ray Castingcited by 3
  5. 2026GIFT: Bootstrapping Image-to-CAD Program Synthesis via Geometric Feedbackcited by 3
  6. 2025CADKnitter: Compositional CAD Generation from Text and Geometry Guidancecited by 2
  7. 2026DualBrep: A Dual-Field Continuous Representation for B-rep Modellingcited by 2
  8. 2026Autoregressive B-Rep Shape Generation with Parametric Surfacescited by 2
  9. 2025BrepLLM: Enabling Large Language Models to Understand Boundary Representationscited by 1
  10. 2026DreamCAD: Scaling Multi-modal CAD Generation using Differentiable Parametric Surfacescited by 1
  11. 2026AutoRegressive Generation with B-rep Holistic Token Sequence Representationcited by 0
  12. 2026Flatten The Complex: Joint B-Rep Generation via Compositional k-Cell Particlescited by 0
8 more
  1. 2026Learning From Design Procedure To Generate CAD Programs for Data Augmentationcited by 0
  2. 2026A2Z-10M+: Geometric Deep Learning with A-to-Z BRep Annotations for AI-Assisted CAD Modeling and Reverse Engineeringcited by 0
  3. 2026HiDiGen: Hierarchical Diffusion for B-Rep Generation with Explicit Topological Constraintscited by 0
  4. 2026Memory-Augmented Reinforcement Learning Agent for CAD Generationcited by 0
  5. 2026Arko-T: A Foundation Model for Text-to-Structured 3D Generationcited by 0
  6. 2026Towards Valid B-Rep Generation: Training-Free Wireframe Anomaly Detection and Repaircited by 0
  7. 2026HiFi-BRep: High-Fidelity Latent Representation for Robust B-Rep Generationcited by 0
  8. 2026Learn the Solid, Not the File: Canonical Inputs for Neural Networks on CAD Boundary Representationscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025GraphBrep: Learning B-Rep in Graph Structure for Efficient CAD Generation
  2. 2025B-Rep Distance Functions (BR-DF): How to Represent a B-Rep Model by Volumetric Distance Functions?
  3. 2025CADDreamer: CAD Object Generation from Single-view Images
  4. 2026GeoFusion-CAD: Structure-Aware Diffusion with Geometric State Space for Parametric 3D Design
  5. 2026Img2CADSeq: Image-to-CAD Generation via Sequence-Based Diffusion
  6. 2025Topology Sculptor, Shape Refiner: Discrete Diffusion Model for High-Fidelity 3D Meshes Generation
  7. 2024CraftsMan3D: High-fidelity Mesh Generation with 3D Native Generation and Interactive Geometry Refiner
  8. 2024Topology-Aware Latent Diffusion for 3D Shape Generation