Deep Learning Model for Point Cloud Classification Based on Graph Convolutional Network

Xujiao Wang; Jie Ma; Nannan Wang; Pengfei Ma; Lichaung Yang

doi:10.3788/LOP56.211004

[1] Lei Z, Xi X H, Wang C et al. Building point clouds extraction from airborne LiDAR data based on decision tree method[J]. Laser & Optoelectronics Progress, 55, 082803(2018).

[2] Tong G F, Du X C, Li Y et al. Three-dimensional point cloud classification of large outdoor scenes based on vertical slice sampling and centroid distance histogram[J]. Chinese Journal of Lasers, 45, 1004001(2018).

[3] Jia Y J, Xiong F G, Han X et al. Multi-scale keypoint detection based on SHOT[J]. Laser & Optoelectronics Progress, 55, 071013(2018).

[4] Charles R Q, Su H, Kaichun M 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. New York: IEEE, 652-660(2017).

[5] Li Y G, Yu R, Shahabi C et al. -02-22)[2019-2-20]. https:∥arxiv., org/abs/1707, 01926(2018).

[6] Yu B, Yin H T. -07-12)[2019-2-20]. https:∥arxiv., org/abs/1709, 04875(2018).

[7] Yan S J, Xiong Y J, Lin D H. Spatial temporal graph convolutional networks for skeleton-based action recognition. [C]∥The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18), February 2-7, 2018, Hilton New Orleans Riverside, New Orleans, Louisiana, USA. New York: AAAI, 7444-7452(2018).

[8] Jain A, Zamir A R, Savarese S et al. Structural-RNN: deep learning on spatio-temporal graphs. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 5308-5317(2016).

[9] Pan S R, Wu J, Zhu X Q et al. Joint structure feature exploration and regularization for multi-task graph classification[J]. IEEE Transactions on Knowledge and Data Engineering, 28, 715-728(2016). http://ieeexplore.ieee.org/document/7302040/

[10] Cignoni P, Callieri M, Corsini M et al. Meshlab: an open-source mesh processing tool[J]. Eurographics Italian Chapter Conference, 2008, 129-136(2008).

[11] 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. New York: IEEE, 1912-1920(2015).

[12] 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. New York: IEEE, 922-928(2015).

[13] Qi C R, Yi L, Su H et al. Pointnet++: deep hierarchical feature learning on point sets in a metric space. [C]∥Advances in Neural Information Processing Systems, December 4-9, 2017, Long Beach Convention Center, Long Beach. New York: NIPS, 5099-5108(2017).

[14] Klokov R, Lempitsky V. Escape from cells: 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. New York: IEEE, 863-872(2017).