mlsanityData to 7 Oct 2026

BRepNet: A topological message passing system for solid models

arXiv 2104.00706 ↗First posted 1 Apr 2021cs.LG cs.CVcited by 92

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Abstract (arXiv)

Boundary representation (B-rep) models are the standard way 3D shapes are described in Computer-Aided Design (CAD) applications. They combine lightweight parametric curves and surfaces with topological information which connects the geometric entities to describe manifolds. In this paper we introduce BRepNet, a neural network architecture designed to operate directly on B-rep data structures, avoiding the need to approximate the model as meshes or point clouds. BRepNet defines convolutional kernels with respect to oriented coedges in the data structure. In the neighborhood of each coedge, a small collection of faces, edges and coedges can be identified and patterns in the feature vectors from these entities detected by specific learnable parameters. In addition, to encourage further deep learning research with B-reps, we publish the Fusion 360 Gallery segmentation dataset. A collection of over 35,000 B-rep models annotated with information about the modeling operations which created each face. We demonstrate that BRepNet can segment these models with higher accuracy than methods working on meshes, and point clouds.

Key facts. Cited by 92 papers in the map (data through 7 Oct 2026). Builds on 13 of its 48 references that are in the map. First posted on arXiv 1 Apr 2021.

Builds on

13 of 48 references.

References · 13

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,223
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  3. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  4. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  5. 2018Deep Learning on Graphs: A Surveycited by 393
  6. 2018ABC: A Big CAD Model Dataset For Geometric Deep Learningcited by 339
  7. 2016Learning Convolutional Neural Networks for Graphscited by 314
  8. 2017Dynamic Edge-Conditioned Filters in Convolutional Neural Networks on Graphscited by 307
  9. 2019Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generationcited by 89
  10. 2020Neural Subdivisioncited by 49
  11. 20173D Shape Segmentation via Shape Fully Convolutional Networkscited by 15
  12. 2018"FabSearch" : A 3D CAD Model Based Search Engine for Sourcing Manufacturing Servicescited by 7
1 more
  1. 2020UV-Net: Learning from Boundary Representationscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 9; 2023: 6; 2024: 22; 2025: 17; 2026: 32.

92 papers.

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  1. 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 266
  2. 2024BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometrycited by 101
  3. 2022SolidGen: An Autoregressive Model for Direct B-rep Synthesiscited by 78
  4. 2021JoinABLe: Learning Bottom-up Assembly of Parametric CAD Jointscited by 77
  5. 2024CAD-MLLM: Unifying Multimodality-Conditioned CAD Generation With MLLMcited by 74
  6. 2021SketchGen: Generating Constrained CAD Sketchescited by 68
  7. 2024CAD-Recode: Reverse Engineering CAD Code from Point Cloudscited by 63
  8. 2024CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise Sketch Instance Guided Attentioncited by 62
  9. 2021Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinderscited by 59
  10. 2024Point-SAM: Promptable 3D Segmentation Model for Point Cloudscited by 49
  11. 2023SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sketch-Extrude Operationscited by 46
  12. 2023D2CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropoutscited by 39
38 more
  1. 2024GenCAD: Image-Conditioned Computer-Aided Design Generation with Transformer-Based Contrastive Representation and Diffusion Priorscited by 36
  2. 2024Img2CAD: Conditioned 3D CAD Model Generation from Single Image with Structured Visual Geometrycited by 35
  3. 2024Img2CAD: Reverse Engineering 3D CAD Models from Images through VLM-Assisted Conditional Factorizationcited by 34
  4. 2023Point2CAD: Reverse Engineering CAD Models from 3D Point Cloudscited by 33
  5. 2022CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representationscited by 30
  6. 2024TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Cloudscited by 29
  7. 2025HoLa: B-Rep Generation using a Holistic Latent Representationcited by 29
  8. 20213D Adversarial Attacks Beyond Point Cloudcited by 26
  9. 2024Make-A-Shape: a Ten-Million-scale 3D Shape Modelcited by 25
  10. 2022Self-Supervised Representation Learning for CADcited by 22
  11. 2024Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Promptscited by 22
  12. 2023SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselinescited by 19
  13. 2022Neural Face Identification in a 2D Wireframe Projection of a Manifold Objectcited by 18
  14. 2022ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generationcited by 17
  15. 2024Split-and-Fit: Learning B-Reps via Structure-Aware Voronoi Partitioningcited by 17
  16. 2024Geometric Deep Learning for Computer-Aided Design: A Surveycited by 14
  17. 2025cadrille: Multi-modal CAD Reconstruction with Reinforcement Learningcited by 14
  18. 2021AutoMate: A Dataset and Learning Approach for Automatic Mating of CAD Assembliescited by 13
  19. 2025BrepGPT: Autoregressive B-rep Generation with Voronoi Half-Patchcited by 12
  20. 2024Wavelet Latent Diffusion (Wala): Billion-Parameter 3D Generative Model with Compact Wavelet Encodingscited by 10
  21. 2025BRepFormer: Transformer-Based B-rep Geometric Feature Recognitioncited by 9
  22. 2025Drawing2CAD: Sequence-to-Sequence Learning for CAD Generation from Vector Drawingscited by 9
  23. 2026Pointer-CAD: Unifying B-Rep and Command Sequences via Pointer-based Edges & Faces Selectioncited by 9
  24. 2026Text-to-CAD Evaluation with CADTestscited by 9
  25. 2025Bringing Attention to CAD: Boundary Representation Learning via Transformercited by 8
  26. 2026CADBench: A Multimodal Benchmark for AI-Assisted CAD Program Generationcited by 7
  27. 2024AIGC for Industrial Time Series: From Deep Generative Models to Large Generative Modelscited by 6
  28. 2025CMT: A Cascade MAR with Topology Predictor for Multimodal Conditional CAD Generationcited by 6
  29. 2025FLARE: Fast Low-rank Attention Routing Enginecited by 6
  30. 2024ContrastCAD: Contrastive Learning-based Representation Learning for Computer-Aided Design Modelscited by 5
  31. 2025NURBGen: High-Fidelity Text-to-CAD Generation through LLM-Driven NURBS Modelingcited by 5
  32. 2022Implicit Conversion of Manifold B-Rep Solids by Neural Halfspace Representationcited by 4
  33. 2024VIRL: Volume-Informed Representation Learning towards Few-shot Manufacturability Estimationcited by 4
  34. 2024BRep Boundary and Junction Detection for CAD Reverse Engineeringcited by 4
  35. 2025MiCADangelo: Fine-Grained Reconstruction of Constrained CAD Models from 3D Scanscited by 4
  36. 2026CADReasoner: Iterative Program Editing for CAD Reverse Engineeringcited by 4
  37. 2026Embodied CAD: Solver-Grounded LLM Agents for Parametric B-Rep Assembly Modelingcited by 4
  38. 2022Material Prediction for Design Automation Using Graph Representation Learningcited by 3

50 most cited of 92 All 92 (JSON)