mlsanityData to 14 Oct 2026

CADOps-Net: Jointly Learning CAD Operation Types and Steps from Boundary-Representations

arXiv 2208.10555 ↗First posted 22 Aug 2022cs.CVcited by 31

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Abstract (arXiv)

3D reverse engineering is a long sought-after, yet not completely achieved goal in the Computer-Aided Design (CAD) industry. The objective is to recover the construction history of a CAD model. Starting from a Boundary Representation (B-Rep) of a CAD model, this paper proposes a new deep neural network, CADOps-Net, that jointly learns the CAD operation types and the decomposition into different CAD operation steps. This joint learning allows to divide a B-Rep into parts that were created by various types of CAD operations at the same construction step; therefore providing relevant information for further recovery of the design history. Furthermore, we propose the novel CC3D-Ops dataset that includes over 37k CAD models annotated with CAD operation type labels and step labels. Compared to existing datasets, the complexity and variety of CC3D-Ops models are closer to those used for industrial purposes. Our experiments, conducted on the proposed CC3D-Ops and the publicly available Fusion360 datasets, demonstrate the competitive performance of CADOps-Net with respect to state-of-the-art, and confirm the importance of the joint learning of CAD operation types and steps.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). Builds on 24 of its 49 references that are in the map. First posted on arXiv 22 Aug 2022.

Builds on

24 of 49 references.

References · 24

  1. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  2. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
  3. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  4. 2018PointConv: Deep Convolutional Networks on 3D Point Cloudscited by 657
  5. 2019Point-Voxel CNN for Efficient 3D Deep Learningcited by 343
  6. 2018ABC: A Big CAD Model Dataset For Geometric Deep Learningcited by 341
  7. 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 271
  8. 2017CSGNet: Neural Shape Parser for Constructive Solid Geometrycited by 106
  9. 2019DensePoint: Learning Densely Contextual Representation for Efficient Point Cloud Processingcited by 104
  10. 2021BRepNet: A topological message passing system for solid modelscited by 95
  11. 2018Supervised Fitting of Geometric Primitives to 3D Point Cloudscited by 91
  12. 2019SpiralNet++: A Fast and Highly Efficient Mesh Convolution Operatorcited by 89
12 more
  1. 2021Computer-Aided Design as Languagecited by 82
  2. 2022SolidGen: An Autoregressive Model for Direct B-rep Synthesiscited by 80
  3. 2020SketchGraphs: A Large-Scale Dataset for Modeling Relational Geometry in Computer-Aided Designcited by 72
  4. 2018MeshCNN: A Network with an Edgecited by 69
  5. 2021SketchGen: Generating Constrained CAD Sketchescited by 68
  6. 2021Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinderscited by 60
  7. 2021Inferring CAD Modeling Sequences Using Zone Graphscited by 50
  8. 2017O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysiscited by 47
  9. 2020Fusion 360 Gallery: A Dataset and Environment for Programmatic CAD Construction from Human Design Sequencescited by 36
  10. 2018A survey on Deep Learning Advances on Different 3D Data Representationscited by 25
  11. 2021PvDeConv: Point-Voxel Deconvolution for Autoencoding CAD Construction in 3Dcited by 20
  12. 2021RPSRNet: End-to-End Trainable Rigid Point Set Registration Network using Barnes-Hut 2D-Tree Representationcited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 11; 2025: 6; 2026: 11.

31 papers.

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  1. 2024CAD-MLLM: Unifying Multimodality-Conditioned CAD Generation With MLLMcited by 81
  2. 2024CAD-Recode: Reverse Engineering CAD Code from Point Cloudscited by 66
  3. 2025Text-to-CadQuery: A New Paradigm for CAD Generation with Scalable Large Model Capabilitiescited by 43
  4. 2024Img2CAD: Reverse Engineering 3D CAD Models from Images through VLM-Assisted Conditional Factorizationcited by 35
  5. 2024TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Cloudscited by 29
  6. 2024Text2CAD: Generating Sequential CAD Models from Beginner-to-Expert Level Text Promptscited by 23
  7. 2023SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselinescited by 19
  8. 2024Geometric Deep Learning for Computer-Aided Design: A Surveycited by 14
  9. 2023SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapescited by 13
  10. 2024PICASSO: A Feed-Forward Framework for Parametric Inference of CAD Sketches via Rendering Self-Supervisioncited by 9
  11. 2025CAD-Coder:Text-Guided CAD Files Code Generationcited by 9
  12. 2026Zero-to-CAD: Agentic Synthesis of Interpretable CAD Programs at Million-Scale Without Real Datacited by 8
19 more
  1. 2024DAVINCI: A Single-Stage Architecture for Constrained CAD Sketch Inferencecited by 6
  2. 2025CMT: A Cascade MAR with Topology Predictor for Multimodal Conditional CAD Generationcited by 6
  3. 2025NURBGen: High-Fidelity Text-to-CAD Generation through LLM-Driven NURBS Modelingcited by 6
  4. 2026CADFS: A Big CAD Program Dataset and Framework for Computer-Aided Design with Large Language Modelscited by 5
  5. 2024BRep Boundary and Junction Detection for CAD Reverse Engineeringcited by 4
  6. 2024MV2Cyl: Reconstructing 3D Extrusion Cylinders from Multi-View Imagescited by 3
  7. 2025A Survey of AI Methods for Geometry Preparation and Mesh Generation in Engineering Simulationcited by 3
  8. 2025Better STEP, a format and dataset for boundary representationcited by 2
  9. 2026SldprtNet: A Large-Scale Multimodal Dataset for CAD Generation in Language-Driven 3D Designcited by 2
  10. 2023Impact of Disentanglement on Pruning Neural Networkscited by 1
  11. 2024Constraint-Aware Feature Learning for Parametric Point Cloudcited by 1
  12. 2026PLLM: Pseudo-Labeling Large Language Models for CAD Program Synthesiscited by 1
  13. 2026FllumaOne: A Code-Native Multimodal CAD Dataset with Executable Programs and Kernel-Validated Feature Historiescited by 1
  14. 2026HierCAD: Hierarchical Text-to-CAD Design via Structure Alignment and Parameter Groundingcited by 1
  15. 2026Masked Topology Modeling for Self-Supervised Learning on Parametric CADcited by 1
  16. 2026A2Z-10M+: Geometric Deep Learning with A-to-Z BRep Annotations for AI-Assisted CAD Modeling and Reverse Engineeringcited by 0
  17. 2026NeuSOGA3D: A Neuro-Symbolic Framework for Explainable 3D Geometric Reconstructioncited by 0
  18. 2026AgenticCADedit: A Stateful, Tool-Mediated Agentic Approach to Multimodal 3D CAD Editingcited by 0
  19. 2026Rethinking Human Intent-to-CAD: Parametric CAD Model Generation via Cooperative Multi-Task Alignment and Spatial-Aware Reinforcement Learningcited by 0