mlsanityData to 7 Oct 2026

PIE-NET: Parametric Inference of Point Cloud Edges

arXiv 2007.04883 ↗First posted 9 Jul 2020cs.CVcited by 67

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Abstract (arXiv)

We introduce an end-to-end learnable technique to robustly identify feature edges in 3D point cloud data. We represent these edges as a collection of parametric curves (i.e.,lines, circles, and B-splines). Accordingly, our deep neural network, coined PIE-NET, is trained for parametric inference of edges. The network relies on a "region proposal" architecture, where a first module proposes an over-complete collection of edge and corner points, and a second module ranks each proposal to decide whether it should be considered. We train and evaluate our method on the ABC dataset, a large dataset of CAD models, and compare our results to those produced by traditional (non-learning) processing pipelines, as well as a recent deep learning based edge detector (EC-NET). Our results significantly improve over the state-of-the-art from both a quantitative and qualitative standpoint.

Key facts. Cited by 67 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jul 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 7; 2023: 11; 2024: 19; 2025: 12; 2026: 11.

67 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. 2023Hierarchical Neural Coding for Controllable CAD Model Generationcited by 84
  4. 2022SolidGen: An Autoregressive Model for Direct B-rep Synthesiscited by 78
  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. 2024CAD-GPT: Synthesising CAD Construction Sequence with Spatial Reasoning-Enhanced Multimodal LLMscited by 52
  11. 2021Engineering Sketch Generation for Computer-Aided Designcited by 49
  12. 2023SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sketch-Extrude Operationscited by 46
38 more
  1. 2022Reconstructing editable prismatic CAD from rounded voxel modelscited by 41
  2. 2023EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecastingcited by 41
  3. 2021PC2WF: 3D Wireframe Reconstruction from Raw Point Cloudscited by 33
  4. 2024TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Cloudscited by 29
  5. 2023NEF: Neural Edge Fields for 3D Parametric Curve Reconstruction from Multi-view Imagescited by 22
  6. 2023NerVE: Neural Volumetric Edges for Parametric Curve Extraction from Point Cloudcited by 22
  7. 2025Sketch and Patch: Efficient 3D Gaussian Representation for Man-Made Scenescited by 20
  8. 2025DTGBrepGen: A Novel B-rep Generative Model through Decoupling Topology and Geometrycited by 20
  9. 2022ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generationcited by 17
  10. 2025cadrille: Multi-modal CAD Reconstruction with Reinforcement Learningcited by 14
  11. 2022AGConv: Adaptive Graph Convolution on 3D Point Cloudscited by 13
  12. 2023SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapescited by 13
  13. 2022HoW-3D: Holistic 3D Wireframe Perception from a Single Imagecited by 12
  14. 20243D Neural Edge Reconstructioncited by 12
  15. 2025BrepGPT: Autoregressive B-rep Generation with Voronoi Half-Patchcited by 12
  16. 2024EdgeGaussians -- 3D Edge Mapping via Gaussian Splattingcited by 10
  17. 2025Curve-Aware Gaussian Splatting for 3D Parametric Curve Reconstructioncited by 9
  18. 2024Self-supervised Learning of Hybrid Part-aware 3D Representations of 2D Gaussians and Superquadricscited by 8
  19. 2025Auto-Regressive Surface Cuttingcited by 8
  20. 2024Parametric Primitive Analysis of CAD Sketches with Vision Transformercited by 7
  21. 2025CLR-Wire: Towards Continuous Latent Representations for 3D Curve Wireframe Generationcited by 7
  22. 2023PlaneRecTR++: Unified Query Learning for Joint 3D Planar Reconstruction and Pose Estimationcited by 6
  23. 2025Revisiting CAD Model Generation by Learning Raster Sketchcited by 6
  24. 2021Multimodal Shape Completion via IMLEcited by 5
  25. 2023PBWR: Parametric Building Wireframe Reconstruction from Aerial LiDAR Point Cloudscited by 5
  26. 2024ContrastCAD: Contrastive Learning-based Representation Learning for Computer-Aided Design Modelscited by 5
  27. 2024Part-aware Shape Generation with Latent 3D Diffusion of Neural Voxel Fieldscited by 5
  28. 20213D Parametric Wireframe Extraction Based on Distance Fieldscited by 4
  29. 2024BRep Boundary and Junction Detection for CAD Reverse Engineeringcited by 4
  30. 2025MiCADangelo: Fine-Grained Reconstruction of Constrained CAD Models from 3D Scanscited by 4
  31. 2026CADReasoner: Iterative Program Editing for CAD Reverse Engineeringcited by 4
  32. 2022BoundED: Neural Boundary and Edge Detection in 3D Point Clouds via Local Neighborhood Statisticscited by 3
  33. 2026UniCAD: A Unified Benchmark and Universal Model for Multi-Modal Multi-Task CADcited by 3
  34. 2024APC2Mesh: Bridging the gap from occluded building faccades to full 3D modelscited by 2
  35. 2024LineGS : 3D Line Segment Representation on 3D Gaussian Splattingcited by 2
  36. 2025PI3DETR: Parametric Instance Detection of 3D Point Cloud Edges With a Geometry-Aware 3DETRcited by 2
  37. 2023PPI-NET: End-to-End Parametric Primitive Inferencecited by 1
  38. 2023Automatic Reverse Engineering: Creating computer-aided design (CAD) models from multi-view imagescited by 1

50 most cited of 67 All 67 (JSON)