[1] Zhou Y, Tuzel O. VoxelNet: end-to-end learning for point cloud based 3D object detection[C], 4490-4499(2018).

[2] Qi C R, Liu W, Wu C X et al. Frustum PointNets for 3D object detection from RGB-D data[C], 918-927(2018).

[3] Ku J, Mozifian M, Lee J et al. Joint 3D proposal generation and object detection from view aggregation[C], 18392975(2018).

[4] Liu Z Z, Chen H Y, Di H J et al. Real-time 6D lidar SLAM in large scale natural terrains for UGV[C], 662-667(2018).

[5] Zhu Y K, Mottaghi R, Kolve E et al. Target-driven visual navigation in indoor scenes using deep reinforcement learning[C], 3357-3364(2017).

[6] Dai R Y, Fang Z J, Gao Y B. Unsupervised monocular depth estimation by fusing dilated convolutional network and SLAM[J]. Laser & Optoelectronics Progress, 57, 061007(2020).

[7] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).

[8] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. https://arxiv.org/abs/1409.1556

[9] Wu Z R, Song S R, Khosla A et al. 3D ShapeNets: a deep representation for volumetric shapes[C], 1912-1920(2015).

[10] Maturana D, Scherer S. VoxNet: a 3D convolutional neural network for real-time object recognition[C], 922-928(2015).

[11] Gadelha M, Wang R, Maji S. Multiresolution tree networks for 3D point cloud processing[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018, 11211, 105-122(2018).

[12] Su H, Maji S, Kalogerakis E et al. Multi-view convolutional neural networks for 3D shape recognition[C], 945-953(2015).

[13] Feng Y F, Zhang Z Z, Zhao X B et al. GVCNN: group-view convolutional neural networks for 3D shape recognition[C], 264-272(2018).

[14] Guo H Y, Wang J Q, Gao Y et al. Multi-view 3D object retrieval with deep embedding network[J]. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society, 25, 5526-5537(2016).

[15] Charles R Q, Hao S, Mo K C et al. PointNet: deep learning on point sets for 3D classification and segmentation[C], 77-85(2017).

[16] Qi C R, Yi L, Su H et al. PointNet++: deep hierarchical feature learning on point sets in a metric space[EB/OL]. https://arxiv.org/abs/1706.02413

[17] Wang Y, Sun Y B, Liu Z W et al. Dynamic graph CNN for learning on point clouds[J]. ACM Transactions on Graphics, 38, 146(2019).

[18] Liu Y C, Fan B, Xiang S M et al. Relation-shape convolutional neural network for point cloud analysis[C], 8887-8896(2019).

[19] Carreira J, Agrawal P, Fragkiadaki K et al. Human pose estimation with iterative error feedback[C], 4733-4742(2016).

[20] Haris M, Shakhnarovich G, Ukita N. Deep back-projection networks for super-resolution[C], 1664-1673(2018).

[21] Liu Z S, Wang L W, Li C T et al. Hierarchical back projection network for image super-resolution[C], 2041-2050(2019).

[22] Li R H, Li X Z, Fu C W et al. PU-GAN: a point cloud upsampling adversarial network[C], 7202-7211(2019).

[23] Vaswani A, Shazeer N, Parmar N et al. Attention is all you need[C], 5998-6008(2017).

[24] Wang X L, Girshick R, Gupta A et al. Non-local neural networks[C], 7794-7803(2018).

[25] Hu J, Shen L, Sun G. Squeeze-and-excitation networks[C], 7132-7141(2018).

[26] Fu J, Liu J, Tian H J et al. Dual attention network for scene segmentation[C], 3141-3149(2019).

[27] Anwar S, Barnes N. Real image denoising with feature attention[C], 3155-3164(2019).

[28] Xie S N, Liu S N, Chen Z Y et al. Attentional ShapeContextNet for point cloud recognition[C], 4606-4615(2018).

[29] Feng M T, Zhang L, Lin X F et al. Point attention network for semantic segmentation of 3D point clouds[EB/OL]. https://arxiv.org/abs/1909.12663

[30] Liu X H, Han Z Z, Wen X et al. L2G auto-encoder: understanding point clouds by local-to-global reconstruction with hierarchical self-attention[C], 989-997(2019).

[31] Hu Q Y, Yang B, Xie L H et al. RandLA-net: efficient semantic segmentation of large-scale point clouds[C], 11105-11114(2020).

[32] 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).

[33] 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, 72(2017).

[34] Wang P S, Sun C Y, Liu Y et al. Adaptive O-CNN: a patch-based deep representation of 3D shapes[J]. ACM Transactions on Graphics, 37, 217(2018).

[35] Kingma D P, Ba J. Adam: a method for stochastic optimization[EB/OL]. https://arxiv.org/abs/1412.6980

[36] He K M, Zhang X Y, Ren S Q et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification[C], 1026-1034(2015).

[37] Engelmann F, Kontogianni T, Leibe B. Dilated point convolutions: on the receptive field size of point convolutions on 3D point clouds[EB/OL]. https://arxiv.org/abs/1907.12046

[38] Klokov R, Lempitsky V. Escape from cells: deep kd-networks for the recognition of 3D point cloud models[C], 863-872(2017).

[39] Li J X, Chen B M, Lee G H. SO-net: self-organizing network for point cloud analysis[C], 9397-9406(2018).

[40] Li Y Y, Bu R, Sun M C et al. PointCNN: convolution on X-transformed points[C](2018).

[41] Shen Y R, Feng C, Yang Y Q et al. Mining point cloud local structures by kernel correlation and graph pooling[C], 4548-4557(2018).

[42] 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, 11208, 56-71(2018).

[43] Thomas H, Qi C R, Deschaud J E et al. KPConv: flexible and deformable convolution for point clouds[C], 6410-6419(2019).

[44] Yan X, Zheng C D, Li Z et al. PointASNL: robust point clouds processing using nonlocal neural networks with adaptive sampling[C], 5588-5597(2020).

[45] 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, 11212, 90-105(2018).

[46] Liu Y C, Fan B, Meng G F et al. DensePoint: learning densely contextual representation for efficient point cloud processing[C], 5238-5247(2019).

[47] Chang A X, Funkhouser T, Guibas L et al. ShapeNet: an information-rich 3D model repository[EB/OL]. https://arxiv.org/abs/1512.03012

[48] Liu X H, Han Z Z, Liu Y S et al. Point2Sequence: learning the shape representation of 3D point clouds with an attention-based sequence to sequence network[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 8778-8785(2019).

[49] Atzmon M, Maron H, Lipman Y. Point convolutional neural networks by extension operators[J]. ACM Transactions on Graphics, 37, 71(2018).