[1] Rusu R B, Marton Z C, Blodow N et al. Persistent point feature histograms for 3D point clouds[C]. //International Conference on Intelligent Autonomous Systems (IAS-10), November 3-7, Baden, Germany. [S.l.: s.n.], 119-128(2009).
[2] Rusu R B, Blodow N, Beetz M. Fast point feature histograms (FPFH) for 3D registration[C]. //2009 IEEE International Conference on Robotics and Automation, May 12-17, 2009, Kobe, Japan., 3212-3217(2009).
[3] Rusu R B, Bradski G, Thibaux R et al. Fast 3D recognition and pose using the Viewpoint Feature Histogram[C]. //2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 18-22, 2010, Taipei, Taiwan, China., 2155-2162(2010).
[8] 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).
[9] 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, California, USA, 5105-5114(2017).
[10] 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).
[13] Xu K, Ba J, Kiros R et al. Show, attend and tell: neural image caption generation with visual attention[C]. //2015 IEEE International Conference on Machine Learning, July 6-11, 2015, Lille, France, 2048-2057(2015).
[14] Wang L, Huang Y C, Hou Y L et al. Graph attention convolution for point cloud semantic segmentation[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 10288-10297(2019).
[15] Li G H, Müller M, Thabet A et al. DeepGCNs: can GCNs go as deep as CNNs?[C]. //2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea, 9266-9275(2019).
[16] 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).
[17] Huang G, Liu Z, van der Maaten L et al. Densely connected convolutional networks[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 2261-2269(2017).
[18] Hamilton W L, Ying Z, Leskovec J et al. Inductive representation learning on large graphs[C]. //Advances in Neural Information Processing Systems, December 4-9, 2017, Long Beach, California, USA, 1024-1034(2017).
[19] Yan S J, Xiong Y J, Lin D H. Spatial temporal garph 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, 7444-7452(2018).
[20] Armeni I, Sener O, Zamir A R et al. 3D semantic parsing of large-scale indoor spaces[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 1534-1543(2016).
[21] Landrieu L, Simonovsky M. Large-scale point cloud semantic segmentation with superpoint graphs[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 4558-4567(2018).
[22] Li 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, Montreal, Candad, 820-830(2018).