research-article

WireframeNet: : A novel method for wireframe generation from point cloud

Authors:

Xiaoping LiuAuthors Info & Claims

Volume 115, Issue C

Pages 226 - 235

https://doi.org/10.1016/j.cag.2023.07.015

Published: 01 February 2024 Publication History

Abstract

Generating wireframe from point clouds is a challenging task. To make this process easier, we introduce WireframeNet, a deep neural network that transforms point clouds into wireframes. The network inputs a set of disordered points and outputs a complete wireframe structure. We use the insight of the medial axis transform to filter the original point cloud, then predict a set of edge points by learning the geometric transformation, and finally analyze the connectivity between the edge points to construct the complete wireframe structure. We train and evaluate publicly available wireframe datasets and compare the results quantitatively and qualitatively with traditional and other deep learning-based methods. Extensive experiments have demonstrated the robust and efficient performance of our proposed WireframeNet for the task of wireframe structure extraction from point clouds.

Graphical abstract

Display Omitted

References

[1]

Guo H., Liu S., Pan H., Liu Y., Tong X., Guo B., ComplexGen: CAD reconstruction by B-rep chain complex generation, ACM Trans Graph 41 (4) (2022).

[2]

Li C., Pan H., Bousseau A., Mitra N.J., Free2CAD: Parsing freehand drawings into CAD commands, ACM Trans Graph 41 (4) (2022) 1–16.

[3]

Lu Z., Guo J., Xiao J., Wang Y., Zhang X., Yan D.-M., Feature curve network extraction via quadric surface fitting, The Eurographics Association, 2019.

[4]

Lu Z., Guo J., Xiao J., Wang Y., Zhang X., Yan D.-M., Extracting cycle-aware feature curve networks from 3D models, Comput Aided Des 131 (2021).

[5]

Chen H., Huang Y., Xie Q., Liu Y., Zhang Y., Wei M., et al., Multiscale feature line extraction from raw point clouds based on local surface variation and anisotropic contraction, IEEE Trans Autom Sci Eng 19 (2) (2021) 1003–1016.

[6]

Zhuang Y., Dou H., Carr N., Ju T., Feature-aligned segmentation using correlation clustering, Comput Vis Media 3 (2) (2017) 147–160.

[7]

Liu Y, D’Aronco S, Schindler K, Wegner JD. PC2WF: 3D Wireframe Reconstruction from Raw Point Clouds. In: International conference on learning representations. 2020.

[8]

Lin Y., Wang C., Chen B., Zai D., Li J., Facet segmentation-based line segment extraction for large-scale point clouds, IEEE Trans Geosci Remote Sens 55 (9) (2017) 4839–4854.

[9]

Wang X., Xu Y., Xu K., Tagliasacchi A., Zhou B., Mahdavi-Amiri A., et al., Pie-net: Parametric inference of point cloud edges, Adv Neural Inf Process Syst 33 (2020) 20167–20178.

[10]

Luo Y, Ren J, Zhe X, Kang D, Xu Yajing WP, Bao L. LC2WF:Learning to Construct 3D Building Wireframes from 3D Line Clouds. In: Proceedings of the British machine vision conference. 2022.

[11]

Blum H., A transformation for extracting new descriptors of shape, in: Wathen-Dunn W. (Ed.), Models for the perception of speech and visual form, MIT Press, Cambridge, 1967, pp. 362–380.

[12]

Koch S, Matveev A, Jiang Z, Williams F, Artemov A, Burnaev E, et al. ABC: A Big CAD Model Dataset For Geometric Deep Learning. In: The IEEE conference on computer vision and pattern recognition. 2019.

[13]

Ren J., Zhang B., Wu B., Huang J., Fan L., Ovsjanikov M., et al., Intuitive and efficient roof modeling for reconstruction and synthesis, ACM Trans Graph (ISSN ) 40 (6) (2021).

[14]

Qi CR, Su H, Mo K, Guibas LJ. Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2017, p. 652–60.

[15]

Qi C.R., Yi L., Su H., Guibas L.J., Pointnet++: Deep hierarchical feature learning on point sets in a metric space, Adv Neural Inf Process Syst 30 (2017).

[16]

Wu Z., Pan S., Chen F., Long G., Zhang C., Philip S.Y., A comprehensive survey on graph neural networks, IEEE Trans Neural Netw Learn Syst 32 (1) (2020) 4–24.

[17]

Ahmed S.M., Tan Y.Z., Chew C.M., Al Mamun A., Wong F.S., Edge and corner detection for unorganized 3d point clouds with application to robotic welding, in: 2018 IEEE/RSJ international conference on intelligent robots and systems, IROS, IEEE, 2018, pp. 7350–7355.

[18]

Hackel T, Wegner JD, Schindler K. Contour detection in unstructured 3D point clouds. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, p. 1610–8.

[19]

Liu L., Chen N., Ceylan D., Theobalt C., Wang W., Mitra N.J., CurveFusion: reconstructing thin structures from RGBD sequences, ACM Trans Graph 37 (6) (2018) 1–12.

[20]

Li Y., Liu S., Yang X., Guo J., Guo J., Guo Y., Surface and edge detection for primitive fitting of point clouds, ACM SIGGRAPH, 2023.

[21]

Bazazian D., Casas J.R., Ruiz-Hidalgo J., Fast and robust edge extraction in unorganized point clouds, in: 2015 international conference on digital image computing: Techniques and applications, IEEE, 2015, pp. 1–8.

[22]

Demarsin K., Vanderstraeten D., Volodine T., Roose D., Detection of closed sharp edges in point clouds using normal estimation and graph theory, Comput Aided Des 39 (4) (2007) 276–283.

[23]

Yang B., Zang Y., Automated registration of dense terrestrial laser-scanning point clouds using curves, ISPRS J Photogramm Remote Sens 95 (2014) 109–121.

[24]

Weber C., Hahmann S., Hagen H., Sharp feature detection in point clouds, in: 2010 shape modeling international conference, IEEE, 2010, pp. 175–186.

[25]

Mérigot Q., Ovsjanikov M., Guibas L.J., Voronoi-based curvature and feature estimation from point clouds, IEEE Trans Vis Comput Graphics 17 (6) (2010) 743–756.

[26]

Hackel T, Wegner JD, Schindler K. Contour detection in unstructured 3D point clouds. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, p. 1610–8.

[27]

Hackel T., Wegner J.D., Schindler K., Joint classification and contour extraction of large 3D point clouds, ISPRS J Photogramm Remote Sens 130 (2017) 231–245.

[28]

Bazazian D., Parés M.E., EDC-Net: Edge detection capsule network for 3D point clouds, Appl Sci 11 (4) (2021) 1833.

[29]

Yu L, Li X, Fu C-W, Cohen-Or D, Heng P-A. Ec-net: an edge-aware point set consolidation network. In: Proceedings of the European conference on computer vision. 2018, p. 386–402.

[30]

Himeur C.-E., Lejemble T., Pellegrini T., Paulin M., Barthe L., Mellado N., PCEDNet: A lightweight neural network for fast and interactive edge detection in 3D point clouds, ACM Trans Graph 41 (1) (2021) 1–21.

[31]

Qi X, Liao R, Jia J, Fidler S, Urtasun R. 3d graph neural networks for rgbd semantic segmentation. In: Proceedings of the IEEE international conference on computer vision. 2017, p. 5199–208.

[32]

Landrieu L, Simonovsky M. Large-scale point cloud semantic segmentation with superpoint graphs. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, p. 4558–67.

[33]

Bi Y, Chadha A, Abbas A, Bourtsoulatze E, Andreopoulos Y. Graph-based object classification for neuromorphic vision sensing. In: Proceedings of the IEEE/CVF international conference on computer vision. 2019, p. 491–501.

[34]

Wang Y., Sun Y., Liu Z., Sarma S.E., Bronstein M.M., Solomon J.M., Dynamic graph cnn for learning on point clouds, ACM Trans Graph 38 (5) (2019) 1–12.

Digital Library

[35]

Shi W, Rajkumar R. Point-gnn: Graph neural network for 3d object detection in a point cloud. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2020, p. 1711–9.

[36]

Zhang Y, Huang D, Wang Y. PC-RGNN: Point cloud completion and graph neural network for 3D object detection. In: Proceedings of the AAAI conference on artificial intelligence, vol. 35, no. 4. 2021, p. 3430–7.

[37]

Kipf TN, Welling M. Variational Graph Auto-Encoders. In: NIPS workshop on Bayesian deep learning. 2016.

[38]

Welling M, Kipf TN. Semi-supervised classification with graph convolutional networks. In: J. international conference on learning representations. 2016.

[39]

Lin C, Li C, Liu Y, Chen N, Choi Y-K, Wang W. Point2skeleton: Learning skeletal representations from point clouds. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021, p. 4277–86.

[40]

Huang K, Wang Y, Zhou Z, Ding T, Gao S, Ma Y. Learning to parse wireframes in images of man-made environments. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, p. 626–35.

[41]

Xue N, Bai S, Wang F, Xia G-S, Wu T, Zhang L. Learning attraction field representation for robust line segment detection. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2019, p. 1595–603.

[42]

Daniels J.I., Ha L.K., Ochotta T., Silva C.T., Robust smooth feature extraction from point clouds, IEEE international conference on shape modeling and applications 2007, IEEE, 2007, pp. 123–136.

[43]

Matveev A., Rakhimov R., Artemov A., Bobrovskikh G., Egiazarian V., Bogomolov E., et al., Def: Deep estimation of sharp geometric features in 3D shapes, ACM Trans Graph 41 (4) (2022) 1–22.

[44]

Ma W, Tan B, Xue N, Wu T, Zheng X, Xia G-S. HoW-3D: Holistic 3D Wireframe Perception from a Single Image. In: International conference on 3D vision. 2022.

[45]

Lin Y., Wang C., Cheng J., Chen B., Jia F., Chen Z., et al., Line segment extraction for large scale unorganized point clouds, ISPRS J Photogramm Remote Sens 102 (2015) 172–183.

[46]

Lin C., Liu L., Li C., Kobbelt L., Wang B., Xin S., et al., Seg-mat: 3d shape segmentation using medial axis transform, IEEE Trans Vis Comput Graphics (2020).

Digital Library

[47]

Pan Y., Wang B., Guo X., Zeng H., Ma Y., Wang W., Q-MAT+: An error-controllable and feature-sensitive simplification algorithm for medial axis transform, Comput Aided Geom Design 71 (2019) 16–29.

[48]

Li B, Liu Y, Wang X. Gradient harmonized single-stage detector. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33, no. 01. 2019, p. 8577–84.

[49]

Tran P.V., Learning to make predictions on graphs with autoencoders, in: 2018 IEEE 5th international conference on data science and advanced analytics, IEEE, 2018, pp. 237–245.

[50]

Yu L, Li X, Fu C-W, Cohen-Or D, Heng P-A. Pu-net: Point cloud upsampling network. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2018, p. 2790–9.

Cited By

Liang Y(2024)SmartGenerator4UI: A Web Interface Element Recognition and HTML Generation System Based on Deep Learning and Image ProcessingProceedings of the 2nd ACM Workshop on Secure and Trustworthy Deep Learning Systems10.1145/3665451.3665528(8-15)Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1145/3665451.3665528
Jorge J(2023)Note computers & graphics issue 115Computers and Graphics10.1016/j.cag.2023.10.018115:C(A1-A3)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.cag.2023.10.018

Index Terms

WireframeNet: A novel method for wireframe generation from point cloud
1. Computing methodologies

Index terms have been assigned to the content through auto-classification.

Recommendations

Multi-scale Edge Detection of Wood Defect Images Based on the Dyadic Wavelet Transform
MVHI '10: Proceedings of the 2010 International Conference on Machine Vision and Human-machine Interface

Multi-scale feature of wavelet and the theory of wavelet transform modulus maxima were researched. A method about multi-scale edge detection based on dyadic wavelet transform was proposed in order to solve the contradiction between noise suppression and ...
Some applications of the Stanford University Drawing System for LSI microprocessor - “a picture is worth a thousand bytes.”
Proceedings of the Symposium on Design Automation and Microprocessors

With the complexity and density of LSI microprocessor designs increasing, Computer Aided Design tools are becoming a necessity rather than a luxury. A significant CAD tool for documentation and bookkeeping is the Stanford University Drawing System (SUDS)...
Introducing solid objects into a constraint modelling system

A constraint modelling system called RASOR allows the constraints between design parameters to be investigated by a designer. Some simple geometric entities are provided. The ACIS solid modeller provides procedures for handling solid objects. An ...

Comments

Information & Contributors

Information

Published In

cover image Computers and Graphics

Computers and Graphics Volume 115, Issue C

Oct 2023

554 pages

ISSN:0097-8493

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 February 2024

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 15 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Liang Y(2024)SmartGenerator4UI: A Web Interface Element Recognition and HTML Generation System Based on Deep Learning and Image ProcessingProceedings of the 2nd ACM Workshop on Secure and Trustworthy Deep Learning Systems10.1145/3665451.3665528(8-15)Online publication date: 2-Jul-2024
https://dl.acm.org/doi/10.1145/3665451.3665528
Jorge J(2023)Note computers & graphics issue 115Computers and Graphics10.1016/j.cag.2023.10.018115:C(A1-A3)Online publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.cag.2023.10.018

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents