• Laser & Optoelectronics Progress
  • Vol. 58, Issue 12, 1215008 (2021)
Jiang’an Wang*, Jiao He, and Dawei Pang
Author Affiliations
  • School of Information Engineering, Chang’an University, Xi’an, Shaanxi 710064, China
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    DOI: 10.3788/LOP202158.1215008 Cite this Article Set citation alerts
    Jiang’an Wang, Jiao He, Dawei Pang. Point Cloud Classification and Segmentation Network Based on Dynamic Graph Convolutional Network[J]. Laser & Optoelectronics Progress, 2021, 58(12): 1215008 Copy Citation Text show less
    References

    [1] Zhao Z Y, Cheng Y L, Shi X S et al. Terrain classification of LiDAR point cloud based on multi-scale features and PointNet[J]. Laser & Optoelectronics Progress, 56, 052804(2019).

    [2] Wang X J, Ma J, Wang N N et al. Deep learning model for point cloud classification based on graph convolutional network[J]. Laser & Optoelectronics Progress, 56, 211004(2019).

    [3] Su H, Maji S, Kalogerakis E et al. Multi-view convolutional neural networks for 3D shape recognition[C]. //2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile., 945-953(2015).

    [4] Maturana D, Scherer S. VoxNet: a 3D convolutional neural network for real-time object recognition[C]. //2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 28-October 2, 2015, Hamburg, Germany, 922-928(2015).

    [5] Charles R Q, Hao S, Mo K C et al. PointNet: deep learning on point sets for 3D classification and segmentation[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, 77-85(2017).

    [6] Qi C R, Yi L, Su H et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[C]. // Proceedings of the 31st International Conference on Neural Information Processing Systems, December 4, 2017, Red Hook, NY, USA, 5105-5114(2017).

    [7] Li J X, Chen B M, Lee G H et al. SO-Net: self-organizing network for point cloud analysis[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA, 9397-9406(2018).

    [8] Xu Y F, Fan T Q, Xu M Y et al. SpiderCNN: deep learning on point sets with parameterized convolutional filters[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11212, 90-105(2018).

    [9] Li Y Y, Bu R, Sun M C et al. PointCNN: convolution on X-transformed points[C]. //Advances in Neural Information Processing Systems, December 3-8, 2018, Montréal, 820-830(2018).

    [10] Wang Y, Sun Y B, Liu Z W et al. Dynamic graph CNN for learning on point clouds[J]. ACM Transactions on Graphics, 38, 1-12(2019).

    [11] Wang B J, Nong L P, Zhang W H et al. 3D point cloud classification and segmentation network based on Spider convolution[J]. Journal of Computer Applications, 40, 1607-1612(2020).

    [12] Shi X S, Cheng Y L, Zhao Z Y et al. Point cloud classification algorithm based on IPTD and SVM[J]. Laser & Optoelectronics Progress, 56, 161002(2019).

    [13] Zhang J Y, Zhao X L, Chen Z et al. Review of semantic segmentation of point cloud based on deep learning[J]. Laser & Optoelectronics Progress, 57, 040002(2020).

    [14] Bai J, Si Q L, Qin F W et al. Lightweight real-time point cloud classification network LightPointNet[J]. Journal of Computer-Aided Design & Computer Graphics, 31, 612-621(2019).

    [15] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

    [16] Wu Z R, Song S R, Khosla A et al. 3D ShapeNets: a deep representation for volumetric shapes[C]. //2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA, 1912-1920(2015).

    [17] Qi C R, Su H, Nießner M et al. Volumetric and multi-view CNNs for object classification on 3D data[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 5648-5656(2016).

    [18] Kingma D P, Ba J. Adam: a method for stochastic optimization[C]. //3rd International Conference on Learning Representations, May 7-9, 2015, San Diego, 13(2015).

    [19] Yi L, Kim V G, Ceylan D et al. A scalable active framework for region annotation in 3D shape collections[J]. ACM Transactions on Graphics, 35, 210(2016).

    [20] Klokov R, Lempitsky V. Escape fromcells: deep Kd-networks for the recognition of 3D point cloud models[C]. //2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy, 863-872(2017).

    Jiang’an Wang, Jiao He, Dawei Pang. Point Cloud Classification and Segmentation Network Based on Dynamic Graph Convolutional Network[J]. Laser & Optoelectronics Progress, 2021, 58(12): 1215008
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