mlsanityData to 7 Oct 2026

ParSeNet: A Parametric Surface Fitting Network for 3D Point Clouds

arXiv 2003.12181 ↗First posted 26 Mar 2020cs.CV cs.LGcited by 96

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Abstract (arXiv)

We propose a novel, end-to-end trainable, deep network called ParSeNet that decomposes a 3D point cloud into parametric surface patches, including B-spline patches as well as basic geometric primitives. ParSeNet is trained on a large-scale dataset of man-made 3D shapes and captures high-level semantic priors for shape decomposition. It handles a much richer class of primitives than prior work, and allows us to represent surfaces with higher fidelity. It also produces repeatable and robust parametrizations of a surface compared to purely geometric approaches. We present extensive experiments to validate our approach against analytical and learning-based alternatives. Our source code is publicly available at: https://hippogriff.github.io/parsenet.

Key facts. Cited by 96 papers in the map (data through 7 Oct 2026). First posted on arXiv 26 Mar 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 15; 2022: 9; 2023: 23; 2024: 18; 2025: 19; 2026: 12.

96 papers.

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  1. 2021DeepCAD: A Deep Generative Network for Computer-Aided Design Modelscited by 266
  2. 2021Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networkscited by 101
  3. 2024BrepGen: A B-rep Generative Diffusion Model with Structured Latent Geometrycited by 101
  4. 2023Hierarchical Neural Coding for Controllable CAD Model Generationcited by 84
  5. 2022SolidGen: An Autoregressive Model for Direct B-rep Synthesiscited by 78
  6. 2024CAD-MLLM: Unifying Multimodality-Conditioned CAD Generation With MLLMcited by 74
  7. 2021SketchGen: Generating Constrained CAD Sketchescited by 68
  8. 2024CAD-Recode: Reverse Engineering CAD Code from Point Cloudscited by 63
  9. 2021CAPRI-Net: Learning Compact CAD Shapes with Adaptive Primitive Assemblycited by 62
  10. 2024CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise Sketch Instance Guided Attentioncited by 62
  11. 2021Point2Cyl: Reverse Engineering 3D Objects from Point Clouds to Extrusion Cylinderscited by 59
  12. 2024CAD-GPT: Synthesising CAD Construction Sequence with Spatial Reasoning-Enhanced Multimodal LLMscited by 52
38 more
  1. 2021Engineering Sketch Generation for Computer-Aided Designcited by 49
  2. 2022Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learningcited by 46
  3. 2023SECAD-Net: Self-Supervised CAD Reconstruction by Learning Sketch-Extrude Operationscited by 46
  4. 2022Surface Reconstruction from Point Clouds: A Survey and a Benchmarkcited by 41
  5. 2022Reconstructing editable prismatic CAD from rounded voxel modelscited by 41
  6. 2023EigenTrajectory: Low-Rank Descriptors for Multi-Modal Trajectory Forecastingcited by 41
  7. 2023D2CSG: Unsupervised Learning of Compact CSG Trees with Dual Complements and Dropoutscited by 39
  8. 2023PartNeRF: Generating Part-Aware Editable 3D Shapes without 3D Supervisioncited by 36
  9. 2021HPNet: Deep Primitive Segmentation Using Hybrid Representationscited by 35
  10. 2024Img2CAD: Conditioned 3D CAD Model Generation from Single Image with Structured Visual Geometrycited by 35
  11. 2024Img2CAD: Reverse Engineering 3D CAD Models from Images through VLM-Assisted Conditional Factorizationcited by 34
  12. 2023Point2CAD: Reverse Engineering CAD Models from 3D Point Cloudscited by 33
  13. 2024TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Cloudscited by 29
  14. 2025HoLa: B-Rep Generation using a Holistic Latent Representationcited by 29
  15. 2023HarmonyView: Harmonizing Consistency and Diversity in One-Image-to-3Dcited by 27
  16. 2021CPFN: Cascaded Primitive Fitting Networks for High-Resolution Point Cloudscited by 24
  17. 2021Learning 3D Dense Correspondence via Canonical Point Autoencodercited by 21
  18. 2023VisCo Grids: Surface Reconstruction with Viscosity and Coarea Gridscited by 20
  19. 2023VecFusion: Vector Font Generation with Diffusioncited by 20
  20. 2023SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselinescited by 19
  21. 2022ComplexGen: CAD Reconstruction by B-Rep Chain Complex Generationcited by 17
  22. 2024Split-and-Fit: Learning B-Reps via Structure-Aware Voronoi Partitioningcited by 17
  23. 2025cadrille: Multi-modal CAD Reconstruction with Reinforcement Learningcited by 14
  24. 2021Neural-IMLS: Self-supervised Implicit Moving Least-Squares Network for Surface Reconstructioncited by 13
  25. 2023SepicNet: Sharp Edges Recovery by Parametric Inference of Curves in 3D Shapescited by 13
  26. 2021DeepCurrents: Learning Implicit Representations of Shapes with Boundariescited by 12
  27. 2024NeurCADRecon: Neural Representation for Reconstructing CAD Surfaces by Enforcing Zero Gaussian Curvaturecited by 12
  28. 2025BrepGPT: Autoregressive B-rep Generation with Voronoi Half-Patchcited by 12
  29. 2021NURBS-Diff: A Differentiable Programming Module for NURBScited by 10
  30. 2023Improving Unsupervised Visual Program Inference with Code Rewriting Familiescited by 10
  31. 2023SENS: Part-Aware Sketch-based Implicit Neural Shape Modelingcited by 8
  32. 2024Parametric Primitive Analysis of CAD Sketches with Vision Transformercited by 7
  33. 2025GenCAD-3D: CAD Program Generation using Multimodal Latent Space Alignment and Synthetic Dataset Balancingcited by 7
  34. 2021PriFit: Learning to Fit Primitives Improves Few Shot Point Cloud Segmentationcited by 6
  35. 2025Revisiting CAD Model Generation by Learning Raster Sketchcited by 6
  36. 2025HaloGS: Loose Coupling of Compact Geometry and Gaussian Splats for 3D Scenescited by 6
  37. 2023PlanarNeRF: Online Learning of Planar Primitives with Neural Radiance Fieldscited by 5
  38. 2024ContrastCAD: Contrastive Learning-based Representation Learning for Computer-Aided Design Modelscited by 5

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