Point Cloud Classification Methods Based on Deep Learning: A Review

Pei Wen; Yinglei Cheng; Wangsheng Yu

doi:10.3788/LOP202158.1600003

[1] Kim K, Kim C, Jang C et al. Deep learning-based dynamic object classification using LiDAR point cloud augmented by layer-based accumulation for intelligent vehicles[J]. Expert Systems with Applications, 167, 113861(2021).

[2] Zermas D, Izzat I, Papanikolopoulos N. Fast segmentation of 3D point clouds: a paradigm on LiDAR data for autonomous vehicle applications[C]. //2017 IEEE International Conference on Robotics and Automation (ICRA), May 29-June 3, 2017, Singapore, 5067-5073(2017).

[3] Ene L T, Næsset E, Gobakken T et al. Large-scale estimation of change in above ground biomass in miombo woodlands using airborne laser scanning and national forest inventory data[J]. Remote Sensing of Environment, 188, 106-117(2017).

[4] Polewski P, Yao W, Heurich M et al. Detection of fallen trees in ALS point clouds using anormalized cut approach trained by simulation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 105, 252-271(2015).

[5] Yang B S, Huang R G, Li J P et al. Automated reconstruction of building LoDs from airborne LiDAR point clouds using an improved morphological scale space[J]. Remote Sensing, 9, 14(2016).

[6] Pan Y, Dong Y Q, Wang D L et al. Three-dimensional reconstruction of structural surface model of heritage bridges using UAV-based photogrammetric point clouds[J]. Remote Sensing, 11, 1204(2019).

[7] Weinmann M, Schmidt A, Mallet C et al. Contextual classification of point cloud data by exploiting individual 3D neigbourhoods[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, II-3/W4, 271-278(2015).

[8] Wang Z, Zhang L Q, Fang T et al. A multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 53, 2409-2425(2015).

[9] Koppula H S, Anand A, Joachims T et al. Semantic labeling of 3D point clouds for indoor scenes[J]. In Advances in Neural Information Processing Systems, 1, 244-252(2011).

[10] Lu Y, Rasmussen C. Simplified Markov random fields for efficient semantic labeling of 3D point clouds[C]. //2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 7-12, 2012, Vilamoura-Algarve, Portugal., 2690-2697(2012).

[11] 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).

[12] Wang C, Samari B, Siddiqi K. Local spectral graph convolution for point set feature learning[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11208, 56-71(2018).

[13] Hua B S, Tran M K, Yeung S K. Pointwise convolutional neural networks[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 984-993(2018).

[14] Veličković P, Cucurull G, Casanova A et al. Graph attention networks[EB/OL]. (2017-10-30)[2020-10-15]. https://arxiv.org/abs/1710.10903

[15] Lalonde J F, Unnikrishnan R, Vandapel N et al. Scale selection for classification of point-sampled 3D surfaces[C]. //Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM’05), June 13-16, 2005, Ottawa, ON, Canada., 285-292(2005).

[16] Lalonde J F, Vandapel N, Huber D F et al. Natural terrain classification using three-dimensional ladar data for ground robot mobility[J]. Journal of Field Robotics, 23, 839-861(2006).

[17] Gao Z H, Liu X W. Support vector machine and object-oriented classification for urban impervious surface extraction from satellite imagery[C]. //2014 The Third International Conference on Agro-Geoinformatics, August 11-14, 2014, Beijing, China., 1-5(2014).

[18] Colgan M, Baldeck C, Féret J B et al. Mapping savanna tree species at ecosystem scales using support vector machine classification and BRDF correction on airborne hyperspectral and LiDAR data[J]. Remote Sensing, 4, 3462-3480(2012).

[19] Miao X, Heaton J S. A comparison of random forest and Adaboost tree in ecosystem classification in east Mojave Desert[C]. //2010 18th International Conference on Geoinformatics, June 18-20, 2010, Beijing, China., 1-6(2010).

[20] Chan J C W, Paelinckx D. Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery[J]. Remote Sensing of Environment, 112, 2999-3011(2008).

[21] Gan Z, Zhong L, Li Y F et al. A random forest based method for urban object classification using lidar data and aerial imagery[C]. //2015 23rd International Conference on Geoinformatics, June 19-21, 2015, Wuhan, China., 6, 19-21(2016).

[22] Wang C S, Shu Q Q, Wang X Y et al. A random forest classifier based on pixel comparison features for urban LiDAR data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 148, 75-86(2019).

[23] Niemeyer J, Rottensteiner F, Soergel U. Contextual classification of lidar data and building object detection in urban areas[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 152-165(2014).

[24] Munoz D, Bagnell J A, Vandapel N et al. Contextual classification with functional max-margin Markov networks[C]. //2009 IEEE Conference on Computer Vision and Pattern Recognition, June 20-25, 2009, Miami, FL, USA., 975-982(2009).

[25] Shapovalov R, Velizhev A, Barinova O. Non-associative Markov networks for 3D point cloud classification[J]. In Proc ISPRS Commission 755 III Symp (PCV), 38, 103-108(2010).

[26] Li W Z, Wang F D, Xia G S. A geometry-attentional network for ALS point cloud classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 26-40(2020).

[27] Ivakhnenko A G. Polynomial theory of complex systems[J]. IEEE Transactions on Systems, Man, and Cybernetics, SMC-1, 364-378(1971).

[28] Hatt M, Parmar C, Qi J Y et al. Machine (deep) learning methods for image processing and radiomics[J]. IEEE Transactions on Radiation and Plasma Medical Sciences, 3, 104-108(2019).

[29] Kong W C, Dong Z Y, Jia Y W et al. Short-term residential load forecasting based on LSTM recurrent neural network[J]. IEEE Transactions on Smart Grid, 10, 841-851(2019).

[30] Sokkhey P, Okazaki T. Development and optimization of deep belief networks applied for academic performance prediction with larger datasets[J]. IEIE Transactions on Smart Processing & Computing, 9, 298-311(2020).

[31] Guo Y L, Wang H Y, Hu Q Y et al. Deep learning for 3D point clouds: a survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5434, 99(2020).

[32] Lateef F, Ruichek Y. Survey on semantic segmentation using deep learning techniques[J]. Neurocomputing, 338, 321-348(2019).

[33] Xie Y X, Tian J J, Zhu X X. Linking points with labels in 3D: a review of point cloud semantic segmentation[J]. IEEE Geoscience and Remote Sensing Magazine, 8, 38-59(2020).

[34] Zhang J Y, Zhao X L, Chen Z et al. A review of deep learning-based semantic segmentation for point cloud[J]. IEEE Access, 7, 179118-179133(2019).

[35] Murase H, Nayar S K. Visual learning and recognition of 3-D objects from appearance[J]. International Journal of Computer Vision, 14, 5-24(1995).

[36] Haala N, Brenner C. Virtual city models from laser altimeter and 2D map data[J]. Photogrammetric Engineering & Remote Sensing, 65, 787-795(1999).

[37] 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).

[38] Feng Y F, Zhang Z Z, Zhao X B et al. GVCNN: group-view convolutional neural networks for 3D shape recognition[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA, 264-272(2018).

[39] Wang C, Pelillo M, Siddiqi K. Dominant set clustering and pooling for multi-view 3D object recognition[C]. //Procedings of the British Machine Vision Conference 2017, September, 2017, London, UK, 1-12(2017).

[40] Yu T, Meng J J, Yuan J S. Multi-view harmonized bilinear network for 3D object recognition[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 186-194(2018).

[41] Endres F, Hess J, Sturm J et al. 3-D mapping with an RGB-D camera[J]. IEEE Transactions on Robotics, 30, 177-187(2014).

[42] Boulch A, Guerry J, le Saux B et al. SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks[J]. Computers & Graphics, 71, 189-198(2018).

[43] Guerry J, Boulch A, le Saux B et al. SnapNet-R: consistent 3D multi-view semantic labeling for robotics[C]. //2017 IEEE International Conference on Computer Vision Workshops (ICCVW), October 22-29, 2017, Venice, Italy, 669-678(2017).

[44] Ma C, Guo Y L, Yang J G et al. Learning multi-view representation with LSTM for 3-D shape recognition and retrieval[J]. IEEE Transactions on Multimedia, 21, 1169-1182(2019).

[45] Biasutti P, Lepetit V, Aujol J F et al. LU-net: an efficient network for 3D LiDAR point cloud semantic segmentation based on end-to-end-learned 3D features and U-net[C]. //2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), October 27-28, 2019, Seoul, Korea (South), 942-950(2019).

[46] Alonso I, Riazuelo L, Montesano L et al. 3D-MiniNet: learning a 2D representation from point clouds for fast and efficient 3D LIDAR semantic segmentation[J]. IEEE Robotics and Automation Letters, 5, 5432-5439(2020).

[47] 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).

[48] 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).

[49] Riegler G, Ulusoy A O, Geiger A. OctNet: learning deep 3D representations at high resolutions[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA, 6620-6629(2017).

[50] Wang P S, Liu Y, Guo Y X et al. O-CNN: octree-based convolutional neural networks for 3D shape analysis[J]. ACM Transactions on Graphics, 36, 1-11(2017).

[51] Xu Y, Hoegner L, Tuttas S et al. Voxel- and graph-based point cloud segmentation of 3D scenes using perceptual grouping laws[J]. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-1/W1, 43-50(2017).

[52] 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, 863-872(2017).

[53] Zeng W, Gevers T. 3DContextNet: K-d tree guided hierarchical learning of point clouds using local and global contextual cues[M]. //Leal-Taixé L, Roth S. Computer vision-ECCV 2018 workshops. Lecture notes in computer science, 11131, 314-330(2019).

[54] Wang L, Huang Y C, Shan J et al. MSNet: multi-scale convolutional network for point cloud classification[J]. Remote Sensing, 10, 612(2018).

[55] Li Y Y, Pirk S, Su H et al. FPNN: field probing neural networks for 3D data[C]. //NIPS’16: Proceedings of the 30th International Conference on Neural Information Processing Systems, December 5, 2016, Red Hook, NY, United States, 307-315(2016).

[56] Le T, Duan Y. PointGrid: a deep network for 3D shape understanding[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA, 9204-9214(2018).

[57] Tchapmi L, Choy C, Armeni I et al. SEGCloud: semantic segmentation of 3D point clouds[C]. //2017 International Conference on 3D Vision (3DV), October 10-12, 2017, Qingdao, China, 537-547(2017).

[58] Meng H Y, Gao L, Lai Y K et al. VV-net: voxel VAE net with group convolutions for point cloud segmentation[C]. //2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South), 8499-8507(2019).

[59] Hegde V, Zadeh R. FusionNet: 3D object classification using multiple data representations[EB/OL]. (2016-07-19)[2020-10-15]. https://arxiv.org/abs/1607.05695

[60] Qi C R, Yi L, Su H et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[C]. //NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems, December 4, 2017, Red Hook, NY, United States, 5099-5108(2017).

[61] Jiang M, Wu Y, Zhao T et al. PointSIFT: a SIFT-like network module for 3D point cloud semantic segmentation[EB/OL]. (2018-07-02)[2020-10-15]. https://arxiv.org/abs/1807.00652

[62] Li J X, Chen B M, Lee G H. 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).

[63] Ma Y X, Guo Y L, Lei Y J et al. 3DMAX-net: a multi-scale spatial contextual network for 3D point cloud semantic segmentation[C]. //2018 24th International Conference on Pattern Recognition (ICPR), August 20-24, 2018, Beijing, China, 1560-1566(2018).

[64] Zhao H S, Jiang L, Fu C W et al. PointWeb: enhancing local neighborhood features for point cloud processing[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA, 5560-5568(2019).

[65] Qiu S, Anwar S, Barnes N. Dense-resolution network for point cloud classification and segmentation[EB/OL]. (2020-05-14)[2020-10-15]. https://arxiv.org/abs/2005.06734?context=cs.CV

[66] Hu Q Y, Yang B, Xie L H et al. RandLA-Net: efficient semantic segmentation of large-scale point clouds[C]. //2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 13-19, 2020, Seattle, WA, USA, 11105-11114(2020).

[67] Scarselli F, Gori M, Tsoi A C et al. The graph neural network model[J]. IEEE Transactions on Neural Networks, 20, 61-80(2009).

[68] Bruna J, Zaremba W, Szlam A et al. Spectral networks and locally connected networks on graphs[EB/OL]. (2013-12-21)[2020-10-15]. https://arxiv.org/abs/1312.6203

[69] Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[EB/OL]. (2016-09-09)[2020-10-15]. https://arxiv.org/abs/1609.02907

[70] Simonovsky M, Komodakis N. Dynamic edge-conditioned filters in convolutional neural networks on graphs[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 29-38(2017).

[71] 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).

[72] Landrieu L, Boussaha M. Point cloud over segmentation with graph-structured deep metric learning[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 7432-7441(2019).

[73] Jiang L, Zhao H S, Liu S et al. Hierarchical point-edge interaction network for point cloud semantic segmentation[C]. //2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South)., 10432-10440(2019).

[74] Te G S, Hu W, Zheng A M et al. RGCNN: regularized graph CNN for point cloud segmentation[C]. //Proceedings of the 26th ACM international conference on Multimedia, October 15, 2018, Seoul Republic of Korea, 746-754(2018).

[75] 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).

[76] Zhang K G, Hao M, Wang J et al. Linked dynamic graph CNN: learning on point cloud via linking hierarchical features[EB/OL]. (2019-04-22)[2020-10-15]. https://arxiv.org/abs/1904.10014

[77] Liu J X, Ni B B, Li C Y et al. Dynamic points agglomeration for hierarchical point sets learning[C]. //2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South)., 7545-7554(2019).

[78] 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 (South), 9266-9275(2019).

[79] Liang Z D, Yang M, Deng L Y et al. Hierarchical depthwise graph convolutional neural network for 3D semantic segmentation of point clouds[C]. //2019 International Conference on Robotics and Automation (ICRA), May 20-24, 2019, Montreal, QC, Canada., 8152-8158(2019).

[80] Lu Q, Chen C, Xie W J et al. PointNGCNN: deep convolutional networks on 3D point clouds with neighborhood graph filters[J]. Computers & Graphics, 86, 42-51(2020).

[81] Zhang W J, Su S Z, Wang B Z et al. Local k-NNs pattern in omni-direction graph convolution neural network for 3D point clouds[J]. Neurocomputing, 413, 487-498(2020).

[82] Nezhadarya E, Taghavi E, Razani R et al. Adaptive hierarchical down-sampling for point cloud classification[C]. //2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 13-19, 2020, Seattle, WA, USA., 12953-12961(2020).

[83] Li Y Y, Bu R, Sun M et al. PointCNN: convolution on χ-transformed points[EB/OL]. (2018-01-23)[2020-10-15]. https://arxiv.org/abs/1801.07791

[84] Lan S Y, Yu R C, Yu G et al. Modeling local geometric structure of 3D point clouds using geo-CNN[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 998-1008(2019).

[85] Mao J G, Wang X G, Li H S. Interpolated convolutional networks for 3D point cloud understanding[C]. //2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South)., 1578-1587(2019).

[86] Komarichev A, Zhong Z C, Hua J. A-CNN: annularly convolutional neural networks on point clouds[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA, 7413-7422(2019).

[87] Rao Y M, Lu J W, Zhou J. Spherical fractal convolutional neural networks for point cloud recognition[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 452-460(2019).

[88] Zhang Z Y, Hua B S, Rosen D W et al. Rotation invariant convolutions for 3D point clouds deep learning[C]. //2019 International Conference on 3D Vision (3DV), September 16-19, 2019, Quebec City, QC, Canada., 204-213(2019).

[89] Lei H, Akhtar N, Mian A. Octree guided CNN with spherical kernels for 3D point clouds[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 9623-9632(2019).

[90] Engelmann F, Kontogianni T, Leibe B. Dilated point convolutions: on the receptive field size of point convolutions on 3D point clouds[J]. (2019-07-28)[2020-10-15]. https://arxiv.org/abs/1907.12046

[91] Wang S L, Suo S, Ma W C et al. Deep parametric continuous convolutional neural networks[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 2589-2597(2018).

[92] 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).

[93] Wu W X, Qi Z, Fuxin L. PointConv: deep convolutional networks on 3D point clouds[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA, 9613-9622(2019).

[94] Shen Y R, Feng C, Yang Y Q et al. Mining point cloud local structures by kernel correlation and graph pooling[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 4548-4557(2018).

[95] Thomas H, Qi C R, Deschaud J E et al. KPConv: flexible and deformable convolution for point clouds[C]. //2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South), 6410-6419(2019).

[96] Liu Y C, Fan B, Xiang S M et al. Relation-shape convolutional neural network for point cloud analysis[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 8887-8896(2019).

[97] Thomas N, Smidt T, Kearnes S M et al. Tensor field networks: rotation- and translation-equivariant neural networks for 3D point clouds[EB/OL]. (2018-02-22)[2020-10-15]. https://arxiv.org/abs/1802.08219?context=cs.NE

[98] Cohen T S, Geiger M, Koehler J et al. Spherical CNNs[EB/OL]. (2018-01-30)[2020-10-15]. https://arxiv.org/abs/1801.10130

[99] Poulenard A, Rakotosaona M J, Ponty Y et al. Effective rotation-invariant point CNN with spherical harmonics kernels[C]. //In 2019 International Conference on 3D Vision (3DV), September 16-19, Quebec City, QC, Canada., 47-56(2019).

[100] Boulch A. ConvPoint: continuous convolutions for point cloud processing[J]. Computers & Graphics, 88, 24-34(2020).

[101] Rensink R A. The dynamic representation of scenes[J]. Visual Cognition, 7, 17-42(2000).

[102] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[C]. //NIPS’17: Proceedings of the 31st International Conference on Neural Information Processing Systems, December 4, 2017, Red Hook, NY, United States, 5998-6008(2017).

[103] Paigwar A, Erkent O, Wolf C et al. Attentional PointNet for 3D-object detection in point clouds[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 16-17, 2019, Long Beach, CA, USA., 1297-1306(2019).

[104] Hu Z Y, Zhang D B, Li S et al. Attention-based relation and context modeling for point cloud semantic segmentation[J]. Computers & Graphics, 90, 126-134(2020).

[105] Yang B, Wang S, Markham A et al. Robust attentional aggregation of deep feature sets for multi-view 3D reconstruction[J]. International Journal of Computer Vision, 128, 53-73(2020).

[106] 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).

[107] Chen C, Fragonara L Z, Tsourdos A. GAPointNet: graph attention based point neural network for exploiting local feature of point cloud[J]. Neurocomputing, 438, 122-132(2021).

[108] Yang J C, Zhang Q, Ni B B et al. Modeling point clouds with self-attention and gumbel subset sampling[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 3318-3327(2019).

[109] Li H, Xiong P, An J et al. Pyramid attention network for semantic segmentation[EB/OL]. (2018-05-25)[2020-10-15]. https://arxiv.org/abs/1805.10180v1

[110] Kang Z H, Li N. PyramNet: point cloud pyramid attention network and graph embedding module for classification and segmentation[EB/OL]. (2019-06-07)[2020-10-15]. https://arxiv.org/abs/1906.03299v1

[111] Chen L Z, Li X Y, Fan D P. LSANet: feature learning on point sets by local spatial attention[EB/OL]. (2019-05-14)[2020-10-15]. https://arxiv.org/abs/1905.05442

[112] Fu J, Liu J, Tian H J et al. Dual attention network for scene segmentation[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 3141-3149(2019).

[113] Xie S N, Liu S N, Chen Z Y et al. Attentional ShapeContextNet for point cloud recognition[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 4606-4615(2018).

[114] Zhao C X, Zhou W H, Lu L et al. Pooling scores of neighboring points for improved 3D point cloud segmentation[C]. //2019 IEEE International Conference on Image Processing (ICIP), September 22-25, 2019, Taipei, Taiwan, China., 1475-1479(2019).

[115] Feng M T, Zhang L, Lin X F et al. Point attention network for semantic segmentation of 3D point clouds[J]. Pattern Recognition, 107, 107446(2020).

[116] Wang J J, Zhong Y F, Zheng Z et al. RSNet: the search for remote sensing deep neural networks in recognition tasks[J]. IEEE Transactions on Geoscience and Remote Sensing, 59, 2520-2534(2021).

[117] Li R H, Li X Z, Heng P A et al. PointAugment: an auto-augmentation framework for point cloud classification[C]. //2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 13-19, 2020, Seattle, WA, USA, 6377-6386(2020).

[118] Schmitt M, Zhu X X. Data fusion and remote sensing: an ever-growing relationship[J]. IEEE Geoscience and Remote Sensing Magazine, 4, 6-23(2016).

[119] Cheng X, Zheng Y H, Zhang J W et al. Multitask multisource deep correlation filter for remote sensing data fusion[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 3723-3734(2020).

[120] Wang Y Y, Zhu X X, Zeisl B et al. Fusing meter-resolution 4-D InSAR point clouds and optical images for semantic urban infrastructure monitoring[J]. IEEE Transactions on Geoscience and Remote Sensing, 55, 14-26(2017).