mlsanityData to 7 Oct 2026

Flatten The Complex: Joint B-Rep Generation via Compositional k-Cell Particles

arXiv 2601.17733 ↗First posted 25 Jan 2026cs.CV cs.GRcited by 0

Not on the map. Why?

Abstract (arXiv)

Boundary Representation (B-Rep) is the widely adopted standard in Computer-Aided Design (CAD) and manufacturing. However, generative modeling of B-Reps remains a formidable challenge due to their inherent heterogeneity as geometric cell complexes, which entangles topology with geometry across cells of varying orders (i.e., k-cells such as vertices, edges, faces). Previous methods typically rely on cascaded sequences to handle this hierarchy, which fails to fully exploit the geometric relationships between cells, such as adjacency and sharing, limiting context awareness and error recovery. To fill this gap, we introduce a novel paradigm that reformulates B-Reps into sets of compositional k-cell particles. Our approach encodes each topological entity as a composition of particles, where adjacent cells share identical latents at their interfaces, thereby promoting geometric coupling along shared boundaries. By decoupling the rigid hierarchy, our representation unifies vertices, edges, and faces, enabling the joint generation of topology and geometry with global context awareness. We synthesize these particle sets using a multi-modal flow matching framework to handle unconditional generation as well as precise conditional tasks, such as 3D reconstruction from single-view or point cloud. Furthermore, the explicit and localized nature of our representation naturally extends to downstream tasks like local in-painting and enables the direct synthesis of non-manifold structures (e.g., wireframes). Extensive experiments demonstrate that our method produces high-fidelity CAD models with superior validity and editability compared to state-of-the-art methods.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 36 of its 51 references that are in the map. First posted on arXiv 25 Jan 2026.

Builds on

36 of 51 references.

References · 36

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2024Scaling Rectified Flow Transformers for High-Resolution Image Synthesiscited by 4,094
  3. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,550
  4. 2020GLU Variants Improve Transformercited by 1,724
  5. 2024Structured 3D Latents for Scalable and Versatile 3D Generationcited by 742
  6. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 552
  7. 20233DShape2VecSet: A 3D Shape Representation for Neural Fields and Generative Diffusion Modelscited by 449
  8. 2018ABC: A Big CAD Model Dataset For Geometric Deep Learningcited by 339
  9. 2020PolyGen: An Autoregressive Generative Model of 3D Meshescited by 267
  10. 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 266
  11. 2025Sonata: Self-Supervised Learning of Reliable Point Representationscited by 108
  12. 2017CSGNet: Neural Shape Parser for Constructive Solid Geometrycited by 106
24 more
  1. 2024BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometrycited by 101
  2. 2021Computer-Aided Design as Languagecited by 82
  3. 2020UCSG-Net -- Unsupervised Discovering of Constructive Solid Geometry Treecited by 77
  4. 2023PolyDiff: Generating 3D Polygonal Meshes with Diffusion Modelscited by 66
  5. 2024FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modificationcited by 64
  6. 2024CAD-Recode: Reverse Engineering CAD Code from Point Cloudscited by 63
  7. 2024Generating CAD Code with Vision-Language Models for 3D Designscited by 61
  8. 2021CSG-Stump: A Learning Friendly CSG-Like Representation for Interpretable Shape Parsingcited by 51
  9. 2021Inferring CAD Modeling Sequences Using Zone Graphscited by 50
  10. 2023Intriguing Properties of Quantization at Scalecited by 48
  11. 2022Reconstructing editable prismatic CAD from rounded voxel modelscited by 41
  12. 2023D2CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropoutscited by 39
  13. 2023PolyDiffuse: Polygonal Shape Reconstruction via Guided Set Diffusion Modelscited by 36
  14. 2025CADCrafter: Generating Computer-Aided Design Models from Unconstrained Imagescited by 31
  15. 2025HoLa: B-Rep Generation using a Holistic Latent Representationcited by 29
  16. 2024CadVLM: Bridging Language and Vision in the Generation of Parametric CAD Sketchescited by 28
  17. 2023Neurosymbolic Models for Computer Graphicscited by 27
  18. 2025CADDreamer: CAD Object Generation from Single-view Imagescited by 22
  19. 2025DTGBrepGen: A Novel B-rep Generative Model through Decoupling Topology and Geometrycited by 20
  20. 2022ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generationcited by 17
  21. 2025AutoBrep: Autoregressive B-Rep Generation with Unified Topology and Geometrycited by 16
  22. 2021Intuitive and Efficient Roof Modeling for Reconstruction and Synthesiscited by 14
  23. 2021AutoMate: A Dataset and Learning Approach for Automatic Mating of CAD Assembliescited by 13
  24. 2025CLR-Wire: Towards Continuous Latent Representations for 3D Curve Wireframe Generationcited by 7

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2026DualBrep: A Dual-Field Continuous Representation for B-rep Modelling
  2. 2025GraphBrep: Learning B-Rep in Graph Structure for Efficient CAD Generation
  3. 2026Generative Modeling with Orbit-Space Particle Flow Matching
  4. 2026Towards Valid B-Rep Generation: Training-Free Wireframe Anomaly Detection and Repair
  5. 2026TORA: Topological Representation Alignment for 3D Shape Assembly
  6. 2026Quartet of Diffusions: Structure-Aware Point Cloud Generation through Part and Symmetry Guidance
  7. 2025B-Rep Distance Functions (BR-DF): How to Represent a B-Rep Model by Volumetric Distance Functions?
  8. 2024Topology-Aware Latent Diffusion for 3D Shape Generation