[1] WU B, ZHOU X, ZHAO S et al. Squeezesegv2: improved model structure and unsupervised domain adaptation for road-object segmentation from a lidar point cloud[C], 4376-4382(2019).

[2] LIU S, WANG Y, YANG X et al. Deep learning in medical ultrasound analysis: a review[J]. Engineering, 5, 261-275(2019).

[3] TCHAPMI L, CHOY C, ARMENI I et al. Segcloud: semantic segmentation of 3d point clouds[C], 537-547(2017).

[4] HU S M, CAI J X, LAI Y K. Semantic labeling and instance segmentation of 3D point clouds using patch context analysis and multiscale processing[J]. IEEE Transactions on Visualization and Computer Graphics, 26, 2485-2498(2018).

[5] KALOGERAKIS E, HERTZMANN A, SINGH K. Learning 3D mesh segmentation and labeling[J]. ACM Transactions on Graphics (TOG), 29, 102: 1-12.(2010).

[6] 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 (TOG), 36, 1-11(2017).

[7] CAO Z, HUANG Q, KARTHIK R. 3D object classification via spherical projections[C], 566-574(2017).

[8] QI C R, SU H, MO K et al. Pointnet: deep learning on point sets for 3d classification and segmentation[C], 652-660(2017).

[9] DENG H, BIRDAL T, ILIC S. Ppfnet: global context aware local features for robust 3d point matching[C], 195-205(2018).

[10] JIANG M, WU Y, ZHAO T et al. Pointsift: a sift-like network module for 3d point cloud semantic segmentation[EB/OL]. http://arxiv.org/abs/1807.00652v1

[11] QI C R, YI L, SU H et al. Pointnet++: Deep hierarchical feature learning on point sets in a metric space[J]. Advances in Neural Information Processing Systems, 30, 1745976(2017).

[12] HE K, ZHANG X, REN S et al. Deep residual learning for image recognition[C], 770-778(2016).

[13] HUANG G, LIU Z, VAN D M L et al. Densely connected convolutional networks[C], 4700-4708(2017).

[14] LEE C Y, XIE S, GALLAGHER P et al. Deeply-supervised nets[C], 562-570(2015).