[1] RASHEED N A, NORDIN M J. Classification and reconstruction algorithms for the archaeological fragments[J]. Journal of King Saud University-Computer and Information Sciences, 32, 883-894(2020).

[2] [2] 2陆正杰， 李纯辉， 耿国华， 等. 基于多特征描述子自适应权重的文物碎片分类［J］. 激光与光电子学进展， 2020， 57（4）： 321-329. doi: 10.3788/LOP57.041511LUZ J， LIC H， GENGG H， et al. Classification of cultural fragments based on adaptive weights of multi-feature descriptions［J］. Laser ＆ Optoelectronics Progress， 2020， 57（4）： 321-329.（in Chinese）. doi: 10.3788/LOP57.041511

[3] CHEN X Z, MA H M, WAN J et al. Multi-view 3D object detection network for autonomous driving[C], 6526-6534(2017).

[4] [4] 4陈苑锋. 视觉深度估计与点云建图研究进展［J］. 液晶与显示， 2021， 36（6）： 896-911. doi: 10.37188/CJLCD.2020-0047CHENY F. Progress of visual depth estimation and point cloud mapping［J］. Chinese Journal of Liquid Crystals and Displays， 2021， 36（6）： 896-911.（in Chinese）. doi: 10.37188/CJLCD.2020-0047

[5] WANG Q, LIU S T, CHANUSSOT J et al. Scene classification with recurrent attention of VHR remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 57, 1155-1167(2019).

[6] GENG G H, LIU J, CAO X et al. Simplification method for 3D Terracotta Warrior fragments based on local structure and deep neural networks[J]. Journal of the Optical Society of America A， Optics， Image Science， and Vision, 37, 1711-1720(2020).

[7] YANG K, CAO X, GENG G H et al. Classification of 3D terracotta warriors fragments based on geospatial and texture information[J]. Journal of Visualization, 24, 251-259(2021).

[8] [8] 8鱼跃华， 张海波， 李昕，等. 基于数据增强的秦俑碎片深度分类模型［J/OL］. 激光与光电子学进展， 2021， doi：10.3788/ lop59.1810010.YUY H， ZHANGH B， LIX， et al. Data Enhanced Depth Classification Model for Terra-Cotta Warriors Fragments ［J/OL］. Laser & Optoelectronics Progress， 2021， doi：10.3788/ lop59.1810010.（in Chinese）

[9] GAO H J, GENG G H, ZENG S. Approach for 3D cultural relic classification based on a low-dimensional descriptor and unsupervised learning[J]. Entropy （Basel， Switzerland）, 22, 1290(2020).

[10] YAO W M, CHU T, TANG W L et al. SPPD： a novel reassembly method for 3D terracotta warrior fragments based on fracture surface information[J]. ISPRS International Journal of Geo-Information, 10, 525(2021).

[11] 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).

[12] 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(2017).

[13] ZHANG Z Y, HUA B S, YEUNG S K. ShellNet： efficient point cloud convolutional neural networks using concentric shells statistics[C], 1607-1616(2019).

[14] LI J X, CHEN B M, LEE G H. SO-net： self-organizing network for point cloud analysis[C], 9397-9406(2018).

[15] [15] 15杨军， 党吉圣. 采用深度级联卷积神经网络的三维点云识别与分割［J］. 光学 精密工程， 2020， 28（5）： 1187-1199. doi: 10.3788/OPE.20202805.1187YANGJ， DANGJ S. Recognition and segmentation of three-dimensional point cloud based on deep cascade convolutional neural network［J］. Opt. Precision Eng.， 2020， 28（5）： 1187-1199.（in Chinese）. doi: 10.3788/OPE.20202805.1187

[16] [16] 16伍锡如， 薛其威. 基于激光雷达的无人驾驶系统三维车辆检测［J］. 光学 精密工程， 2022， 30（4）： 489-497. doi: 10.37188/OPE.20223004.0489WUX R， XUEQ W. 3D vehicle detection for unmanned driving systerm based on lidar［J］. Opt. Precision Eng.， 2022， 30（4）： 489-497.（in Chinese）. doi: 10.37188/OPE.20223004.0489

[17] LIU J, CAO X, ZHANG P C et al. AMS-net： an attention-based multi-scale network for classification of 3D terracotta warrior fragments[J]. Remote Sensing, 13, 3713(2021).

[18] [18] 18徐哲， 耿杰， 蒋雯， 等. 联合训练生成对抗网络的半监督分类方法［J］. 光学 精密工程， 2021， 29（5）： 1127-1135. doi: 10.37188/OPE.20212905.1127XUZ， GENGJ， JIANGW， et al. Co-training generative adversarial networks for semi-supervised classification method［J］. Opt. Precision Eng.， 2021， 29（5）： 1127-1135.（in Chinese）. doi: 10.37188/OPE.20212905.1127

[19] ACHLIOPTAS P, DIAMANTI O, MITLIAGKAS I et al. Learning representations and generative models for 3d point clouds[C], 421-428(27).

[20] YANG Y Q, FENG C, SHEN Y R et al. FoldingNet： point cloud auto-encoder via deep grid deformation[C], 206-215(2018).

[21] LI C L, ZAHEER M, ZHANG Y et al. Point cloud GAN[webpage]. 2018： arXiv, 1810-05795. https：//arxiv.org/abs/1810.05795

[22] 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).

[23] HASSANI K, HALEY M. Unsupervised multi-task feature learning on point clouds[C], 8159-8170(2019).

[24] SCHROFF F, KALENICHENKO D, PHILBIN J. FaceNet： a unified embedding for face recognition and clustering[C], 815-823(2015).

[25] KIHYUK Sohn. Improved deep metric learning with multi-class n-pair loss objective[C], 1857-1865(5).

[26] DU G G, ZHOU M Q, YIN C L et al. Classifying fragments of terracotta warriors using template-based partial matching[J]. Multimedia Tools and Applications, 77, 19171-19191(2018).