research-article

Surface and Edge Detection for Primitive Fitting of Point Clouds

Authors:

Yanwen GuoAuthors Info & Claims

SIGGRAPH '23: ACM SIGGRAPH 2023 Conference Proceedings

Article No.: 44, Pages 1 - 10

https://doi.org/10.1145/3588432.3591522

Published: 23 July 2023 Publication History

Abstract

Fitting primitives for point cloud data to obtain a structural representation has been widely adopted for reverse engineering and other graphics applications. Existing segmentation-based approaches only segment primitive patches but ignore edges that indicate boundaries of primitives, leading to inaccurate and incomplete reconstruction. To fill the gap, we present a novel surface and edge detection network (SED-Net) for accurate geometric primitive fitting of point clouds. The key idea is to learn parametric surfaces (including B-spline patches) and edges jointly that can be assembled into a regularized and seamless CAD model in one unified and efficient framework. SED-Net is equipped with a two-branch structure to extract type and edge features and geometry features of primitives. At the core of our network is a two-stage feature fusion mechanism to utilize the type, edge and geometry features fully. Precisely detected surface patches can be employed as contextual information to facilitate the detection of edges and corners. Benefiting from the simultaneous detection of surfaces and edges, we can obtain a parametric and compact model representation. This enables us to represent a CAD model with predefined primitive-specific meshes and also allows users to edit its shape easily. Extensive experiments and comparisons against previous methods demonstrate our effectiveness and superiority.

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Cited By

Fang ZZhuang CLu ZWang YLiu LXiao J(2025)BGPSeg: Boundary-Guided Primitive Instance Segmentation of Point CloudsIEEE Transactions on Image Processing10.1109/TIP.2025.354058634(1454-1468)Online publication date: 2025
https://doi.org/10.1109/TIP.2025.3540586
Li SZhang LJiang WElberink S(2025)Plane segmentation from point clouds using the detail preserving optimal-vector-fieldOptics & Laser Technology10.1016/j.optlastec.2025.112580185(112580)Online publication date: Jul-2025
https://doi.org/10.1016/j.optlastec.2025.112580
Wan HZhao F(2024)A Hierarchical Neural Network for Point Cloud Segmentation and Geometric Primitive FittingEntropy10.3390/e2609071726:9(717)Online publication date: 23-Aug-2024
https://doi.org/10.3390/e26090717
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Index Terms

Surface and Edge Detection for Primitive Fitting of Point Clouds
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
      1. Parametric curve and surface models
      2. Point-based models

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cover image ACM Conferences

SIGGRAPH '23: ACM SIGGRAPH 2023 Conference Proceedings

July 2023

911 pages

ISBN:9798400701597

DOI:10.1145/3588432

Copyright © 2023 ACM.

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Published: 23 July 2023

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SIGGRAPH '23: Special Interest Group on Computer Graphics and Interactive Techniques Conference

August 6 - 10, 2023

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Cited By

Fang ZZhuang CLu ZWang YLiu LXiao J(2025)BGPSeg: Boundary-Guided Primitive Instance Segmentation of Point CloudsIEEE Transactions on Image Processing10.1109/TIP.2025.354058634(1454-1468)Online publication date: 2025
https://doi.org/10.1109/TIP.2025.3540586
Li SZhang LJiang WElberink S(2025)Plane segmentation from point clouds using the detail preserving optimal-vector-fieldOptics & Laser Technology10.1016/j.optlastec.2025.112580185(112580)Online publication date: Jul-2025
https://doi.org/10.1016/j.optlastec.2025.112580
Wan HZhao F(2024)A Hierarchical Neural Network for Point Cloud Segmentation and Geometric Primitive FittingEntropy10.3390/e2609071726:9(717)Online publication date: 23-Aug-2024
https://doi.org/10.3390/e26090717
Fan RHe FLiu YSong YFan LYan X(2024)A parametric and feature-based CAD dataset to support human-computer interaction for advanced 3D shape learningIntegrated Computer-Aided Engineering10.3233/ICA-24074432:1(73-94)Online publication date: 18-Oct-2024
https://doi.org/10.3233/ICA-240744
Wang XWang LWu HXiao GXu K(2024)Parametric Primitive Analysis of CAD Sketches With Vision TransformerIEEE Transactions on Industrial Informatics10.1109/TII.2024.341335820:10(12041-12050)Online publication date: Oct-2024
https://doi.org/10.1109/TII.2024.3413358
Guo YLi YRen DZhang XLi JPu LMa CZhan XGuo JWei MZhang YYu PYang SJi DYe HSun HLiu YChen YZhu JLiu H(2024)LiDAR-Net: A Real-Scanned 3D Point Cloud Dataset for Indoor Scenes2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.02076(21989-21999)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.02076
Li PGuo JLi HBenes BYan D(2024)SfmCAD: Unsupervised CAD Reconstruction by Learning Sketch-based Feature Modeling Operations2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)10.1109/CVPR52733.2024.00447(4671-4680)Online publication date: 16-Jun-2024
https://doi.org/10.1109/CVPR52733.2024.00447
Chen ZShi YNan LXiong ZZhu X(2024)PolyGNN: Polyhedron-based graph neural network for 3D building reconstruction from point cloudsISPRS Journal of Photogrammetry and Remote Sensing10.1016/j.isprsjprs.2024.09.031218(693-706)Online publication date: Dec-2024
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Cai SYe YCao JChen Z(2024)FACEGraphical Models10.1016/j.gmod.2024.101230136:COnline publication date: 1-Dec-2024
https://dl.acm.org/doi/10.1016/j.gmod.2024.101230
Ning XShi QLiu YJin HWang YZhang XGuo J(2024)DRC-NET: Density Reweighted Convolution Network for Edge Curve ExtractionPattern Recognition and Computer Vision10.1007/978-981-97-8490-5_29(406-420)Online publication date: 7-Nov-2024
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