research-article

PCEDNet: A Lightweight Neural Network for Fast and Interactive Edge Detection in 3D Point Clouds

Authors:

Chems-Eddine Himeur,

Thibault Lejemble,

Thomas Pellegrini,

Mathias Paulin,

Nicolas MelladoAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 41, Issue 1

Article No.: 10, Pages 1 - 21

https://doi.org/10.1145/3481804

Published: 10 November 2021 Publication History

Abstract

In recent years, Convolutional Neural Networks (CNN) have proven to be efficient analysis tools for processing point clouds, e.g., for reconstruction, segmentation, and classification. In this article, we focus on the classification of edges in point clouds, where both edges and their surrounding are described. We propose a new parameterization adding to each point a set of differential information on its surrounding shape reconstructed at different scales. These parameters, stored in a Scale-Space Matrix (SSM), provide a well-suited information from which an adequate neural network can learn the description of edges and use it to efficiently detect them in acquired point clouds. After successfully applying a multi-scale CNN on SSMs for the efficient classification of edges and their neighborhood, we propose a new lightweight neural network architecture outperforming the CNN in learning time, processing time, and classification capabilities. Our architecture is compact, requires small learning sets, is very fast to train, and classifies millions of points in seconds.

References

[1]

Syeda Mariam Ahmed, Yan Zhi Tan, Chee-Meng Chew, Abdullah Al Mamun, and Fook Seng Wong. 2018. Edge and corner detection for unorganized 3D point clouds with application to robotic welding. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 7350–7355.

[2]

Marc Alexa, Johannes Behr, Daniel Cohen-Or, Shachar Fleishman, David Levin, and Claudio T. Silva. 2001. Point set surfaces. In Proceedings of the Conference on Visualization (VIS’01). IEEE Computer Society, Washington, DC, 21–28. Retrieved from http://dl.acm.org/citation.cfm?id=601671.601673.

Digital Library

[3]

Pierre Alliez, Simon Giraudot, Clément Jamin, Florent Lafarge, Quentin Mérigot, Jocelyn Meyron, Laurent Saboret, Nader Salman, Shihao Wu, and Necip Fazil Yildiran. 2021. Point set processing. In CGAL User and Reference Manual (5.2.1 ed.). CGAL Editorial Board. Retrieved from https://doc.cgal.org/5.2.1/Manual/packages.html#PkgPointSetProcessing3.

[4]

Dena Bazazian, Josep R. Casas, and Javier Ruiz-Hidalgo. 2015. Fast and robust edge extraction in unorganized point clouds. In Proceedings of the International Conference on Digital Image Computing: Techniques and Applications (DICTA). IEEE, 1–8.

[5]

Matthew Berger, Andrea Tagliasacchi, Lee M. Seversky, Pierre Alliez, Gaël Guennebaud, Joshua A. Levine, Andrei Sharf, and Claudio T. Silva. 2017. A survey of surface reconstruction from point clouds. Comput. Graph. Forum 36, 1 (Jan. 2017), 301–329.

Digital Library

[6]

Alexandre Boulch. 2019. Generalizing discrete convolutions for unstructured point clouds.

[7]

Alexandre Boulch, Joris Guerry, Bertrand Le Saux, and Nicolas Audebert. 2018. SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks. Comput. Graph. 71 (2018), 189–198.

[8]

Alexandre Boulch and Renaud Marlet. 2016. Deep learning for robust normal estimation in unstructured point clouds. Comput. Graph. Forum 35, 5 (2016), 281–290.

[9]

Alexandre Boulch, Bertrand Le Saux, and Nicolas Audebert. 2017. Unstructured point cloud semantic labeling using deep segmentation networks. In Eurographics Workshop on 3D Object Retrieval, Ioannis Pratikakis, Florent Dupont, and Maks Ovsjanikov (Eds.). The Eurographics Association.

Digital Library

[10]

M. M. Bronstein and I. Kokkinos. 2010. Scale-invariant heat kernel signatures for non-rigid shape recognition. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 1704–1711.

[11]

Jiazhou Chen, Gaël Guennebaud, Pascal Barla, and Xavier Granier. 2013a. Non-oriented MLS gradient fields. Comput. Graph. Forum 32, 8 (2013), 98–109. arXiv:https://onlinelibrary. wiley.com/doi/pdf/10.1111/cgf.12164.

[12]

Jiazhou Chen, Gaël Guennebaud, Pascal Barla, and Xavier Granier. 2013b. Non-oriented MLS gradient fields. Comput. Graph. Forum 32, 8 (2013), 98–109. arXiv:https://onlinelibrary. wiley.com/doi/pdf/10.1111/cgf.12164.

[13]

Davide Chicco and Giuseppe Jurman. 2020. The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation. BMC Genom. 21 (2020).

[14]

Paolo Cignoni, Marco Callieri, Massimiliano Corsini, Matteo Dellepiane, Fabio Ganovelli, and Guido Ranzuglia. 2008. MeshLab: An open-source mesh processing tool. In Eurographics Italian Chapter Conference, Vittorio Scarano, Rosario De Chiara, and Ugo Erra (Eds.). The Eurographics Association.

[15]

Forrester Cole, Aleksey Golovinskiy, Alex Limpaecher, Heather Stoddart Barros, Adam Finkelstein, Thomas Funkhouser, and Szymon Rusinkiewicz. 2008. Where do people draw lines?ACM Trans. Graph. (Proc. SIGGRAPH) 27, 3 (Aug. 2008).

Digital Library

[16]

Angela Dai, Angel X. Chang, Manolis Savva, Maciej Halber, Thomas Funkhouser, and Matthias Nießner. 2017. ScanNet: Richly-annotated 3D reconstructions of indoor scenes. In Proceedings of the Computer Vision and Pattern Recognition (CVPR). IEEE.

[17]

Joel Daniels II, Tilo Ochotta, Linh K. Ha, and Cláudio T. Silva. 2008. Spline-based feature curves from point-sampled geometry. Vis. Comput. 24, 6 (2008), 449–462.

Digital Library

[18]

Kris Demarsin, Denis Vanderstraeten, Tim Volodine, and Dirk Roose. 2007. Detection of closed sharp edges in point clouds using normal estimation and graph theory. Comput.-aided Des. 39, 4 (2007), 276–283.

Digital Library

[19]

J. Digne, S. Valette, and R. Chaine. 2018. Sparse geometric representation through local shape probing. IEEE Trans. Vis. Comput. Graph. 24, 7 (July 2018), 2238–2250.

[20]

Leandro A. F. Fernandes and Manuel M. Oliveira. 2012. A general framework for subspace detection in unordered multidimensional data. Pattern Recog. 45, 9 (2012), 3566–3579.

Digital Library

[21]

Shachar Fleishman, Daniel Cohen-Or, and Cláudio T. Silva. 2005. Robust moving least-squares fitting with sharp features. ACM Trans. Graph. 24, 3 (July 2005), 544–552.

Digital Library

[22]

Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics. 249–256.

[23]

Gaël Guennebaud, Marcel Germann, and Markus Gross. 2008. Dynamic sampling and rendering of algebraic point set surfaces. Comput. Graph. Forum 27, 2 (2008), 653–662.

[24]

Gaël Guennebaud and Markus Gross. 2007. Algebraic point set surfaces. ACM Trans. Graph. 26, 3 (July 2007).

Digital Library

[25]

Gaël Guennebaud, Benoît Jacob, et al. 2010. Eigen v3. Retrieved from http://eigen.tuxfamily.org.

[26]

Paul Guerrero, Yanir Kleiman, Maks Ovsjanikov, and Niloy J. Mitra. 2018. PCPNet: Learning local shape properties from raw point clouds. Comput. Graph. Forum 37, 2 (2018), 75–85.

[27]

Stefan Gumhold, Xinlong Wang, and Rob Macleod. 2001. Feature extraction from point clouds. In Proceedings of the 10th International Meshing Roundtable. 293–305.

[28]

Timo Hackel, N. Savinov, L. Ladicky, Jan D. Wegner, K. Schindler, and M. Pollefeys. 2017. SEMANTIC3D.NET: A new large-scale point cloud classification benchmark. In ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. IV-1-W1. 91–98.

[29]

Timo Hackel, Jan D. Wegner, and Konrad Schindler. 2016. Contour detection in unstructured 3D point clouds. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[30]

E. Kalogerakis, M. Averkiou, S. Maji, and S. Chaudhuri. 2017. 3D shape segmentation with projective convolutional networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 6630–6639.

[31]

Sebastian Koch, Albert Matveev, Zhongshi Jiang, Francis Williams, Alexey Artemov, Evgeny Burnaev, Marc Alexa, Denis Zorin, and Daniele Panozzo. 2019. ABC: A big CAD model dataset for geometric deep learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 9593–9603.

[32]

Thibault Lejemble, David Coeurjolly, Loic Barthe, and Nicolas Mellado. 2021. Stable and efficient differential estimators on oriented point clouds. Comput. Graph. Forum 40 (July 2021), 205–216. https://doi.org/10.1111/cgf.14368

[33]

M. Li and K. Hashimoto. 2017. Curve set feature-based robust and fast pose estimation algorithm. Sensors 1782, 17 (2017).

[34]

Yangyan Li, Rui Bu, Mingchao Sun, Wei Wu, Xinhan Di, and Baoquan Chen. 2018. PointCNN: Convolution on X-transformed points. In Advances in Neural Information Processing Systems 31, S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett (Eds.). Curran Associates, Inc., 820–830. Retrieved from http://papers.nips.cc/paper/7362-pointcnn-convolution-on-x-transformed-points.pdf.

Digital Library

[35]

Yangbin Lin, Cheng Wang, Jun Cheng, Bili Chen, Fukai Jia, Zhonggui Chen, and Jonathan Li. 2015. Line segment extraction for large scale unorganized point clouds. ISPRS J. Photogramm. Rem. Sens. 102 (2015), 172–183.

[36]

Tony Lindeberg. 1993. Scale-space Theory in Computer Vision. Kluwer Academic Publishers, Norwell, MA.

Digital Library

[37]

Eric-Tuan Lê, Iasonas Kokkinos, and Niloy J. Mitra. 2020. Going deeper with lean point networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]

Daniel Maturana and Sebastian Scherer. 2015. VoxNet: A 3D convolutional neural network for real-time object recognition. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. 922–928.

[39]

Nicolas Mellado, Matteo Dellepiane, and Roberto Scopigno. 2015. Relative scale estimation and 3D registration of multi-modal geometry using growing least squares. IEEE Trans. Vis. Comput. Graph. 22, 9 (2015), 2160–2173.

Digital Library

[40]

Nicolas Mellado, Gaël Guennebaud, Pascal Barla, Patrick Reuter, and Christophe Schlick. 2012. Growing least squares for the analysis of manifolds in scale-space. Comput. Graph. Forum 31, 5 (Aug. 2012), 1691–1701.

Digital Library

[41]

Nicolas Mellado, Thibault Lejemble, Gaël Guennebaud, and Pascal Barla. 2020. Ponca: a Point Cloud Analysis Library. Retrieved from https://github.com/poncateam/ponca/.

[42]

Aikaterini Mitropoulou and Andreas Georgopoulos. 2019. An automated process to detect edges in unorganized point clouds. ISPRS Ann. Photogramm., Rem. Sens. Spatial Inf. Sci. IV-2/W6 (2019), 99–105. https://doi.org/10.5194/isprs-annals-IV-2-W6-99-2019

[43]

Aron Monszpart, Nicolas Mellado, Gabriel J. Brostow, and Niloy J. Mitra. 2015. RAPter: Rebuilding man-made scenes with regular arrangements of planes. ACM Trans. Graph. 34, 4 (2015).

Digital Library

[44]

Charly Mourglia, Valentin Roussellet, Loic Barthe, Nicolas Mellado, Mathias Paulin, David Vanderhaeghe, et al. 2021. Radium Engine.

[45]

Q. Mérigot, M. Ovsjanikov, and L. J. Guibas. 2011. Voronoi-based curvature and feature estimation from point clouds. IEEE Trans. Vis. Comput. Graph. 17, 6 (2011), 743–756.

Digital Library

[46]

Keith Wei Liang Nguyen, A. Aprilia, Ahmad Khairyanto, Wee Ching Pang, Gerald Gim Lee Seet, and Shu Beng Tor. 2018. Edge detection from point cloud of worn parts. In Proceedings of the 3rd International Conference on Progress in Additive Manufacturing. 595–600.

[47]

Huan Ni, Xiangguo Lin, Xiaogang Ning, and Jixian Zhang. 2016. Edge detection and feature line tracing in 3D-point clouds by analyzing geometric properties of neighborhoods. Rem. Sens. 8, 9 (2016), 710.

[48]

Mark Pauly, Richard Keiser, and Markus Gross. 2003. Multi-scale feature extraction on point-sampled surfaces. Comput. Graph. Forum 22, 3 (2003), 281–289.

[49]

Charles Ruizhongtai Qi, Wei Liu, Chenxia Wu, Hao Su, and Leonidas J. Guibas. 2018. Frustum PointNets for 3D object detection from RGB-D data. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR).

[50]

C. R. Qi, H. Su, M. Kaichun, and L. J. Guibas. 2017. PointNet: Deep learning on point sets for 3D classification and segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 77–85.

[51]

Charles Ruizhongtai Qi, Hao Su, Matthias Nießner, Angela Dai, Mengyuan Yan, and Leonidas J. Guibas. 2016. Volumetric and multi-view CNNs for object classification on 3D data. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR).

[52]

Charles R. Qi, Li Yi, Hao Su, and Leonidas J. Guibas. 2017. PointNet++: Deep hierarchical feature learning on point sets in a metric space. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17). Curran Associates Inc. 5105–5114. Retrieved from http://dl.acm.org/citation.cfm?id=3295222.3295263.

Digital Library

[53]

Andrés Serna, Beatriz Marcotegui, François Goulette, and Jean-Emmanuel Deschaud. 2014. Paris-rue-Madame database: A 3D mobile laser scanner dataset for benchmarking urban detection, segmentation and classification methods. In Proceedings of the 4th International Conference on Pattern Recognition, Applications and Methods ICPRAM. Retrieved from https://hal.archives-ouvertes.fr/hal-00963812.

Digital Library

[54]

H. Su, V. Jampani, D. Sun, S. Maji, E. Kalogerakis, M. Yang, and J. Kautz. 2018. SPLATNet: Sparse lattice networks for point cloud processing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2530–2539.

[55]

Hang Su, Subhransu Maji, Evangelos Kalogerakis, and Erik G. Learned-Miller. 2015. Multi-view convolutional neural networks for 3D shape recognition. In Proceedings of the International Conference on Computer Vision.

Digital Library

[56]

H. Thomas, C. R. Qi, J. Deschaud, B. Marcotegui, F. Goulette, and L. Guibas. 2019. KPConv: Flexible and deformable convolution for point clouds. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). 6410–6419.

[57]

E. Moscoso Thompson, G. Arvanitis, K. Moustakas, N. Hoang-Xuan, E. R. Nguyen, M. Tran, T. Lejemble, L. Barthe, N. Mellado, C. Romanengo, S. Biasotti, and B. Falcidieno. 2019. Feature curve extraction on triangle meshes. In Proceedings of the Eurographics Workshop on 3D Object Retrieval, Silvia Biasotti, Guillaume Lavoué, and Remco Veltkamp (Eds.). The Eurographics Association.

[58]

Chu Wang, Babak Samari, and Kaleem Siddiqi. 2018. Local spectral graph convolution for point set feature learning. In Computer Vision - ECCV 2018-15th European Conference, Munich, Germany, September 8–14, 2018, Proceedings, Part IV (Lecture Notes in Computer Science), Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, and Yair Weiss (Eds.), Vol. 11208. Springer, 56–71.

[59]

Xiaogang Wang, Yuelang Xu, Kai Xu, Andrea Tagliasacchi, Bin Zhou, Ali Mahdavi-Amiri, and Hao Zhang. 2020. PIE-NET: Parametric inference of point cloud edges. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 20167–20178. Retrieved from https://proceedings.neurips.cc/paper/2020/file/e94550c93cd70fe748e6982b3439ad3b-Paper.pdf.

[60]

Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E. Sarma, Michael M. Bronstein, and Justin M. Solomon. 2019. Dynamic graph CNN for learning on point clouds. ACM Trans. Graph. 38, 5.

Digital Library

[61]

Christopher Weber, Stefanie Hahmann, and Hans Hagen. 2010. Sharp feature detection in point clouds. In Proceedings of the Shape Modeling International Conference (SMI’10). IEEE Computer Society, 175–186.

Digital Library

[62]

Christopher Weber, Stefanie Hahmann, Hans Hagen, and Georges-Pierre Bonneau. 2012. Sharp feature preserving MLS surface reconstruction based on local feature line approximations. Graph. Mod. 74, 6 (2012), 335–345.

Digital Library

[63]

Martin Weinmann, Boris Jutzi, and Clément Mallet. 2013. Feature relevance assessment for the semantic interpretation of 3D point cloud data. In ISPRS Annals, Vol. 5.

[64]

Andrew P. Witkin. 1987. Scale-space filtering. In Readings in Computer Vision. Elsevier, 329–332.

Digital Library

[65]

Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao. 2015. 3D shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1912–1920.

[66]

S. Xia and R. Wang. 2017. A fast edge extraction method for mobile lidar point clouds. IEEE Geosci. Rem. Sens. Lett. 14, 8 (2017), 1288–1292.

[67]

Yifan Xu, Tianqi Fan, Mingye Xu, Long Zeng, and Yu Qiao. 2018. SpiderCNN: Deep learning on point sets with parameterized convolutional filters. In Proceedings of the European Conference on Computer Vision (ECCV). 87–102.

Digital Library

[68]

L. Yu, X. Li, C. Fu, D. Cohen-Or, and P. Heng. 2018. PU-Net: Point cloud upsampling network. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2790–2799.

[69]

Lequan Yu, Xianzhi Li, Chi-Wing Fu, Daniel Cohen-Or, and Pheng-Ann Heng. 2018. EC-net: An edge-aware point set consolidation network. In Computer Vision – ECCV 2018, Vittorio Ferrari, Martial Hebert, Cristian Sminchisescu, and Yair Weiss (Eds.). Springer International Publishing, Cham, 398–414.

[70]

Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R. Salakhutdinov, and Alexander J. Smola. 2017. Deep sets. In Advances in Neural Information Processing Systems 30, I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Eds.). Curran Associates, Inc., 3391–3401. Retrieved from http://papers.nips.cc/paper/6931-deep-sets.pdf.

Digital Library

[71]

Aite Zhao, Jianbo Li, and Manzoor Ahmed. 2020. SpiderNet: A spiderweb graph neural network for multi-view gait recognition. Knowl.-based Syst. 206 (2020), 106273.

[72]

Zhirong Wu, S. Song, A. Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and J. Xiao. 2015. 3D ShapeNets: A deep representation for volumetric shapes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1912–1920.

[73]

Yin Zhou and Oncel Tuzel. 2018. VoxelNet: End-to-end learning for point cloud based 3D object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 4490–4499.

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https://doi.org/10.3390/rs16010190
Jiao XLv CYi RZhao JPan ZWu ZLiu Y(2024)MSL-Net: Sharp Feature Detection Network for 3D Point CloudsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.334690730:9(6433-6446)Online publication date: Sep-2024
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Index Terms

PCEDNet: A Lightweight Neural Network for Fast and Interactive Edge Detection in 3D Point Clouds
1. Computing methodologies
  1. Computer graphics
    1. Shape modeling
      1. Point-based models
      2. Shape analysis
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 41, Issue 1

February 2022

178 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3484929

Editor:
Carol O'Sullivan
Trinity College Dublin, Ireland

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Publication History

Published: 10 November 2021

Accepted: 01 August 2021

Revised: 01 June 2021

Received: 01 October 2020

Published in TOG Volume 41, Issue 1

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Jiao XLv CYi RZhao JPan ZWu ZLiu Y(2024)MSL-Net: Sharp Feature Detection Network for 3D Point CloudsIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2023.334690730:9(6433-6446)Online publication date: Sep-2024
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