Computer Science ›› 2019, Vol. 46 ›› Issue (9): 47-58.doi: 10.11896/j.issn.1002-137X.2019.09.006

• Surveys • Previous Articles     Next Articles

3D Shape Feature Extraction Method Based on Deep Learning

ZHOU Yan, ZENG Fan-zhi, WU Chen, LUO Yue, LIU Zi-qin   

  1. (Department of Computer Science,FoShan University,Foshan,Guangdong 528000,China)
  • Received:2019-05-15 Online:2019-09-15 Published:2019-09-02

Abstract: Research on extracting 3D shape features with low dimension and high discriminating ability can solve the problem such as classification,retrieval of 3D shape data.With the continuous development of deep learning,3D shape feature extraction method combineds with deep learning has become a research hotspot.Combining deep learning with traditional 3D shape feature extraction methods can not only break through the bottleneck of non-deep learningme-thods,but also improve the accuracy of 3D shape data classification,retrieval and other tasks,especially when 3D shape is non-rigid body.However,deep learning is still developing,and there are still problems that require a large number of training samples.Therefore,how to effectively extract 3D shape features by using deep learning methods has become the research focus and difficulty in the field of computer vision.At present,most researchers focus on improving the ability of neural network to extract features by improving network structure,training methods and other aspects.First,the re-levant deep learning model are introduced,and there are some new ideas about the network improvement and training methods.Second,the feature extraction methods of rigid body and non-rigid body based on deep learning are comprehensively expounded which combined with the development of deep learning and 3D shape feature extraction methods,and the current deep learning methods for the 3D shape feature extraction are described.And then,the current situation of the existing 3D shape retrieval system and the similarity calculation method are described.Finally,the current problems of 3D shape feature extraction methods are introduced,and its future development trend are explored.

Key words: 3D shape, Feature extraction, Deep learning, Neural networks

CLC Number: 

  • TP391.4
[1]WEI X Z,QIU S Q,ZHU L,et al.Toward Support-Free 3D Printing:A Skeletal Approach for Partitioning Models[J].IEEE Transactions on Visualization and Computer Graphics,2018,24(10):2799-2812.
[2]CHU C H,WANG I J,WANG J B,et al.3D parametric human face modeling for personalized product design[J].Advanced Engineering Informatics,2017,32:202-223.
[3]FRADI A,LOUHICHI B,MAHJOUB M A,et al.3D ObjectRetrieval Based on Similarity Calculation in 3D Computer Aided Design Systems[C]//Proceedings of 2017 IEEE/ACS 14th International Conference on Computer Systems and Applications.Hammamet:IEEE,2017:160-165.
[4]SHAN H,ZHANG Y,YANG Q,et al.3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network[J].IEEE Transactions on Medical Imaging,2018,37(6):1522-1534.
[5]HU W F,ZHAO S,REN Y,et al.3D model dynamic cutting technology based on game engine[C]//Proceedings of Annual Acis International Conference on Computer and Information Scie-nce.Wuahn:IEEE,2017:1-6.
[6]YANG Y B,LIN H,ZHU Q.Content-Based 3D Model Retrie-val:A Survey[J].CHINESE JOURNAL OF COMPUTERS,2004,27(10):1297-1310.(in Chinese)杨育彬,林珲,朱庆.基于内容的三维模型检索综述[J].计算机学报,2004,27(10):1297-1310.
[7]SU H,MAJI S,KALOGERAKIS E,et al.Multi-view Convolutional Neural Networks for 3D Shape Recognition[C]//Proceedings of International Conference on Computer Vision.Santiago:IEEE,2015:945-953.
[8]FENG Y,ZHANG Z,ZHAO X,et al.GVCNN:Group-ViewConvolutional Neural Networks for 3D Shape Recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:264-272.
[9]KANEZAKI A,MATSUSHITA Y,NISHIDA Y,et al.Rota-tionNet:Joint Object Categorization and Pose Estimation Using Multiviews from Unsupervised Viewpoints[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:5010-5019.
[10]WU Z,SONG S,KHOSLA A,et al.3D ShapeNets:A deep representation for volumetric shapes[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE,2015:1912-1920.
[11]LI Y,PIRK S,SU H,et al.FPNN:Field Probing Neural Networks for 3D Data[C]//Proceedings of Neural Information Processing Systems.Barcelona:Curran Associates,2016:307-315.
[12]QI C R,SU H,MO K,et al.PointNet:Deep Learning on Point Sets for 3D Classification and Segmentation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Honolulu:IEEE,2017:77-85.
[13]QI C R,YI L,SU H,et al.PointNet++:Deep Hierarchical Feature Learning on Point Sets in a Metric Space[C]//Procee-dings of Neural Information Processing Systems.Long Beach:Curran Associates,2017:5099-5108.
[14]FENG Y,FENG Y,YOU H,et al.MeshNet:Mesh Neural Network for 3D Shape Representation[C]//Proceedings of AAAI Conference on Artificial Intelligence.Honolulu:AAAI,2019.
[15]TATARCHENKO M,DOSOVITSKIY A,BROX T,et al.Octree Generating Networks:Efficient Convolutional Architectures for High-resolution 3D Outputs[C]//Proceedings of International Conference on Computer Vision.Venice:IEEE,2017:2107-2115.
[16]LI H S,SUN L,WU Y J,et al.Survey on Feature Extraction Techniques for Non-Rigid 3D Shape Retrieval[J].Journal of Software,2018,29(2):483-505.(in Chinese)李海生,孙莉,武玉娟,等.非刚性三维模型检索特征提取技术研究[J].软件学报,2018,29(2):483-505.
[17]GUO Y L.Depth feature representation of 3d shape data[J].CCF Computer Vision Newsletter,2017(2):8-11.(in Chinese)郭裕兰.三维形状数据的深度特征表示[J].CCF计算机视觉专委简报,2017(2):8-11.
[18]LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-basedlearning applied to document recognition[J].Proceedings of the IEEE,1998,86(11):2278-2324.
[19]HINTON G E,OSINDERO S,TEH Y W,et al.A fast learning algorithm for deep belief nets[J].Neural Computation,2006,18(7):1527-1554.
[20]HINTON G E,SALAKHUTDINOV R.Reducing the dimen-sionality of data with neural networks[J].Science,2006,313(5786):504-507.
[21]RUMELHART D E,HINTON G E,WILLIAMS R J,et al.Learning representations by back-propagating errors[J].Nature,1988,323(6088):696-699.
[22]GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Generative Adversarial Nets[C]//Proceedings of Neural Information Processing Systems.Montreal:Curran Associates,2014:2672-2680.
[23]ZOPH B,LE Q V.Searching for Activation Functions[C]//Proceedings of International Conference on Learning Representations.Vancouver,2018.
[24]KRIZHEVSKY A,SUTSKEVER I,HINTON G E,et al.ImageNet Classification with Deep Convolutional Neural Networks[J].Neural Information Processing Systems,2012,141(5):1097-1105.
[25]SIMONYAN K,ZISSERMAN A.Very Deep Convolutional Networks for Large-Scale Image Recognition[C]//Proceedings of International Conference on Learning Representations.San Diego,2015.
[26]SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Boston:IEEE,2015:1-9.
[27]HE K,ZHANG X,REN S,et al.Deep Residual Learning for Image Recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE,2016:770-778.
[28]HUANG G,LIU Z,LAURENS V D M,et al.Densely Connec-ted Convolutional Networks[C]//Proceedings of IEEE Confe-rence on Computer Vision and Pattern Recognition.Honolulu:IEEE,2017:2261-2269.
[29]YANG Y,ZHONG Z,SHEN T,et al.Convolutional NeuralNetworks with Alternately Updated Clique[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:2413-2422.
[30]SUN K,XIAO B,LIU D,et al.HRNet:Deep High-Resolution Representation Learning for Human Pose Estimation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE,2019:5693-5703.
[31]CHEN X,DUAN Y,HOUTHOOFT R,et al.InfoGAN:interpretable representation learning by information maximizing generative adversarial nets[C]//Proceedings of Neural Information Processing Systems.Barcelona:Curran Associates,2016:2180-2188.
[32]LIU C,CHEN L C,SCHROFF F,et al.Auto-DeepLab:Hierarchical Neural Architecture Search for Semantic Image Segmentation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE,2019:82-92.
[33]TAN M,LE Q V.EfficientNet:Rethinking Model Scaling for Convolutional Neural Networks[C]//Proceedings of International Conference on Machine Learning.Long Beach:ACM,2019.
[34]IANDOLA F N,HAN S,MOSKEWICZ M W,et al.Squeeze-Net:AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size[C]//Proceedings of International Conference on Learning Representations.Toulon,2017.
[35]SANDLER M,HOWARD A,ZHU M,et al.MobileNetV2:Inverted Residuals and Linear Bottlenecks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:4510-4520.
[36]ZHANG X,ZHOU X,LIU M,et al.ShuffleNet:An Extremely Efficient Convolutional Neural Network for Mobile Devices[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:6848-6856.
[37]XIE G,WANG J,ZHANG T,et al.Interleaved StructuredSparse Convolutional Neural Networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:8847-8856.
[38]MEHTA S,RASTEGARI M,CASPI A,et al.ESPNet:Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation[C]//Proceedings of The European Conference on Computer Vision.Munich:Springer,2018:552-568.
[39]GOYAL P,DOLLAR P,GIRSHICK R B,et al.Accurate,Large Minibatch SGD:Training ImageNet in 1 Hour[J].arXiv:Computer Vision and Pattern Recognition,2017.
[40]KINGMA D P,BA J.Adam:A Method for Stochastic Optimization[C]//Proceedings of International Conference on Learning Representations.San Diego,2015.
[41]DUCHI J C,HAZAN E,SINGER Y,et al.Adaptive Subgradient Methods for Online Learning and Stochastic Optimization[J].Journal of Machine Learning Research,2011,12(7):2121-2159.
[42]LUO L,XIONG Y,LIU Y,et al.Adaptive Gradient Methods with Dynamic Bound of Learning Rate[C]//Proceedings of International Conference on Learning Representations.New Orleans,2019.
[43]FURUYA T,OHBUCHI R.Deep Aggregation of Local 3D Geometric Features for 3D Model Retrieval[C]//Proceedings of British Machine Vision Conference.York:BMVA Press,2016.
[44]XIA Q,LI S,HAO A M,et al.Deep Learning for Digital Geometry Processing and Analysis:A Review[J].Journal of Computer Research and Development,2019,56(1):155-182.(in Chinese)夏清,李帅,郝爱民,等.基于深度学习的数字几何处理与分析技术研究进展[J].计算机研究与发展,2019,56(1):155-182.
[45]KAZHDAN M M,FUNKHOUSER T A,RUSINKIEWICZ S,et al.Rotation invariant spherical harmonic representation of 3D shape descriptors[C]//Proceedings of Eurographics Symposium on Geometry Processing.Aachen:The Eurographics Association,2003:156-164.
[46]CHEN D Y,TIAN X P,SHEN Y T,et al.On Visual Similarity Based 3D Model Retrieval[J].Computer Graphics Forum,2003,22(3):223-232.
[47]MAHMOUDI M,SAPIRO G.Three-dimensional point cloudrecognition via distributions of geometric distances[J].Graphical Models,2009,71(1):22-31.
[48]SUN J,OVSJANIKOV M,GUIBAS L.A Concise and Provably Informative Multi-Scale Signature Based on Heat Diffusion[J].Computer Graphics Forum,2009,28(5):1383-1392.
[49]AUBRY M,SCHLICKEWEI U,CREMERS D.The wave kernel signature:A quantum mechanical approach to shape analysis[C]//Proceedings of International Conference on Computer Vision Workshops.Barcelona:IEEE,2011:1626-1633.
[50]KLOKOV R,LEMPITSKY V.Escape from Cells:Deep Kd-Networks for the Recognition of 3D Point Cloud Models[C]//Proceedings of International Conference on Computer Vision.Venice:IEEE,2017:863-872.
[51]WANG Y,SUN Y,LIU Z,et al.Dynamic Graph CNN forLearning on Point Clouds[J].arXiv:Computer Vision and Pattern Recognition,2018.
[52]JOHNS E,LEUTENEGGER S,DAVISON A J.Pairwise Decomposition of Image Sequences for Active Multi-View Recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Las Vegas:IEEE,2016:3813-3822.
[53]WANG C,SAMARI B,SIDDIQI K,et al.Local Spectral Graph Convolution for Point Set Feature Learning[C]//Proceedings of European Conference on Computer Vision.Munich:Springer,2018:56-71.
[54]LI J,CHEN B M,LEE G H,et al.SO-Net:Self-Organizing Network for Point Cloud Analysis[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:9397-9406.
[55]ZHAO Y,BIRDAL T,DENG H,et al.3D Point-Capsule Net-works[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE,2019:1009-1018.
[56]LAN S,YU R,YU G,et al.Modeling Local Geometric Structure of 3D Point Clouds using Geo-CNN[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE,2019:998-1008.
[57]KATO H,USHIKU Y,HARADA T.Neural 3D Mesh Renderer[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:3907-3916.
[58]GROUEIX T,FISHER M,KIM V G,et al.AtlasNet:A Papier-Ma^ché Approach to Learning 3D Surface Generation[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:216-224.
[59]ZHOU Y,ZENG F,QIAN J,et al.3D shape classification andretrieval based on polar view[J].Information Sciences,2019,474:205-220.
[60]JIANG J,BAO D,CHEN Z,et al.MLVCNN:Multi-Loop-View Convolutional Neural Network for 3D Shape Retrieval[C]//Proceedings of AAAI Conference on Artificial Intelligence.Honolulu:AAAI,2019.
[61]LI Y M,XUE K X,GAO Z,et al.3-D Model Retrieval Algorithm Based on Residual Network[J].COUMPUTER SCIENCE,2019,46(3):148-153.(in Chinese)李荫民,薛凯心,高赞,等.基于残差网络的三维模型检索算法[J].计算机科学,2019,46(3):148-153.
[62]MATURANA D,SCHERER S.VoxNet:A 3D ConvolutionalNeural Network for real-time object recognition[C]//Proceedings of Intelligent Robots and Systems.Hamburg:IEEE,2015:922-928.
[63]YANG J,WANG S,ZHOU P.Recognition and Classification for Three-Dimensional Model Based on Deep Voxel Convolution Neural Network[J].Acta Optica Sinica,2019,39(4):314-324.(in Chinese)杨军,王顺,周鹏.基于深度体素卷积神经网络的三维模型识别分类[J].光学学报,2019,39(4):314-324.
[64]WANG C,CHENG M,SOHEL F,et al.NormalNet:A voxel-based CNN for 3D object classification and retrieval[J].Neurocomputing,2019,323:139-147.
[65]KUMAWAT S,RAMAN S.LP-3DCNN:Unveiling Local Phase in 3D Convolutional Neural Networks[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE,2019:4903-4912.
[66]RIEGLER G,ULUSOY A O,GEIGER A,et al.OctNet:Learning Deep 3D Representations at High Resolutions[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Honolulu:IEEE,2017:6620-6629.
[67]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,2017,36(4):72:1-72:11.
[68]ZHOU Y,ZENG F.2D compressive sensing and multi-feature fusion for effective 3D shape retrieval[J].Information Sciences,2017,409-410:101-120.
[69]HEGDE V,ZADEH R.Fusionnet:3D Object Classification Using Multiple Data Representations[J].arXiv:Computer Vision and Pattern Recognition,2016.
[70]YOU H,FENG Y,JI R,et al.PVNet:A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition[J].Acm Multimedia,2018:1310-1318.
[71]YOU H,FENG Y,ZHAO X,et al.PVRNet:Point-View Relation Neural Network for 3D Shape Recognition[C]//Proceedings of AAAI Conference on Artificial Intelligence.Honolulu:AAAI,2019.
[72]BOYER E,BRONSTEIN A M,BRONSTEIN M M,et al.SHREC 2011:robust feature detection and description benchmark[C]//Proceedings of Eurographics Conference on 3D Object Retrieval.Llandudno:The Eurographics Association,2011:71-78.
[73]ZENG H,LIU Y,LIU J,et al.Non-Rigid 3D Model Retrieval Based on Quadruplet Convolutional Neural Networks[J].IEEE Access,2018,6:76087-76097.
[74]LUCIANO L,HAMZA A B.Deep learning with geodesic moments for 3D shape classification[J].Pattern Recognition Letters,2018:182-190.
[75]HAN Z,LIU Z,HAN J,et al.Unsupervised Learning of 3-D Local Features From Raw Voxels Based on a Novel Permutation Voxelization Strategy[J].IEEE Transactions on Systems,Man,and Cybernetics,2019,49(2):481-494.
[76]SINHA A,BAI J,RAMANI K,et al.Deep Learning 3D Shape Surfaces Using Geometry Images[C]//Proceedings of European Conference on Computer Vision.Amsterdam:Springer,2016:223-240.
[77]BAI J,SI Q L,QIN F W.3D Model Classification and Retrieval Based on CNN and Voting Scheme[J].Journal of Computer-Aided Design & Computer Graphics,2019,31(2):123-134.(in Chinese)白静,司庆龙,秦飞巍.基于卷积神经网络和投票机制的三维模型分类与检索[J].计算机辅助设计与图形学学报,2019,31(2):123-134.
[78]BU S,WANG L,HAN P,et al.3D shape recognition and re-trieval based on multi-modality deep learning[J].Neurocomputing,2017,259(2017):183-193.
[79]ZENG H,LIU Y,LI S,et al.Convolutional Neural NetworkBased Multi-feature Fusion for Non-rigid 3D Model Retrieval[J].Journal of Information Processing Systems,2018,14(1):176-190.
[80]HE T,HUANG H,YI L,et al.GeoNet:Deep Geodesic Networks for Point Cloud Analysis[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Long Beach:IEEE,2019:6888-6897.
[81]YU L,LI X,FU C,et al.PU-Net:Point Cloud Upsampling Network[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Salt Lake City:IEEE,2018:2790-2799.
[82]PAQUET E,RIOUX M.Nefertiti:A Query by Content Software for Three-Dimensional Models Databases Management[C]//International Conference on Recent Advances in 3-D Digital Imaging and Modeling (3DIM '97).Ottawa,Ontario,Canada:DBLP,1997.
[83]MIN P,HALDERMAN J A,KAZHDAN M M,et al.Early experiences with a 3D model search engine[C]//Proceedings of International Conference on 3D Web Technology.New York:ACM,2003:7-18.
[84]SAGARA H,TAKANO S,OKADA Y,et al.3D Model Data Retrieval System Using KAZE Feature for Accepting 2D Image as Query[C]//Proceedings of International Conference on Complex,Intelligent,and Software Intensive Systems.Fukuoka:IEEE,2016:617-622.
[85]SINTUNATA V,AOKI T.3D object retrieval system usingskewness database[C]//Proceedings of International Conference on Control,Decision and Information Technologies.St.Julian's:IEEE,2016:123-128.
[86]KAO C.Shape-based 3D model retrieval system.International Journal of Computers and Applications.https://www.tandfonline.com/doi/abs/10.1080/1206212X.2016.1226541.
[87]WU Z Q,ZHU Z M,LIU W,et al.Retrieving 3D Models from Institutional Repository[J].Data Analysis and Knowledge Discovery,2017,1(1):73-80.(in Chinese)吴志强,祝忠明,刘巍,等.机构知识库三维模型检索与展示技术研究与实践[J].数据分析与知识发现,2017,1(1):73-80.
[88]LI H S,LAI L,CAI Q,et al.3D Model Storage and Shape Distribution in Hadoop Environment[J].Journal of Computer Research and Development,2014,51(S2):18-29.(in Chinese)李海生,赖龙,蔡强,等.Hadoop环境下三维模型的存储及形状分布特征提取[J].计算机研究与发展,2014,51(S2):18-29.
[89]BIASOTTI S,CERRI A,BRONSTEIN A M,et al.Quantifying 3D Shape Similarity Using Maps:Recent Trends,Applications and Perspectives[C]//Proceedings of eurographics.Strasbourg:The Eurographics Association,2014:135-159.
[90]TABIA H,LAGA H,PICARD D,et al.Covariance Descriptors for 3D Shape Matching and Retrieval[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.Columbus:IEEE,2014:4185-4192.
[91]LI B,LU Y,LI C,et al.A comparison of 3D shape retrievalmethods based on a large-scale benchmark supporting multimodal queries[J].Computer Vision and Image Understanding,2015,131:1-27.
[92]WANG J,MA K,SINGH V K,et al.BodyPrint:Pose Invariant 3D Shape Matching of Human Bodies[C]//Proceedings of International Conference on Computer Vision.Santiago:IEEE,2015:1591-1599.
[93]Princeton University.The Princeton shape benchmark[EB/OL].(2005-03-15) .http://shape.cs.princeton.edu/benchmark/.
[94]SHILANE P,MIN P,KZAHDAN M,et al.The Princeton shape benchmark[C]//Proceedings of Conference on the Shape Modeling Applications.Genova:IEEE,2004.
[95]PAN S J,YANG Q.A Survey on Transfer Learning[J].IEEE Transactions on Knowledge and Data Engineering,2010,22(10):1345-1359.
[96]SUTTON R S,BARTO A G.Reinforcement Learning:An Introduction[J].IEEE Transactions on Neural Networks,1998,9(5):1054-1054.
[97]HAN Z,LIU Z,HAN J,et al.Mesh Convolutional RestrictedBoltzmann Machines for Unsupervised Learning of Features With Structure Preservation on 3-D Meshes[J].IEEE Transactions on Neural Networks,2017,28(10):2268-2281.
[98]WANG J T,ZHAO L,QI X B.Face Recognition Method Based on Adaptive 3D Morphable Model and Multiple Manifold Discriminant Analysis[J].Computer Science,2017,44(S1):232-235,239.(in Chinese)王渐韬,赵丽,齐兴斌.自适应三维形变模型结合流形分析的人脸识别方法[J].计算机科学,2017,44(S1):232-235,239.
[99]RUSSAKOVSKY O,DENG J,SU H,et al.ImageNet LargeScale Visual Recognition Challenge[J].International Journal of Computer Vision,2015,115(3):211-252.
[100]CHANG A X,FUNKHOUSER T A,GUIBAS L J,et al.ShapeNet:An Information-Rich 3D Model Repository[J].arXiv:1512.03012,2015.
[101]DUAN P.Anisotropy Radial Basis Function Spatial Interpola-tion Model Research for 3D Spatial Field[J].Acta Geodaetica et Cartographica Sinica,2018,47(12):1696.(in Chinese)段平.三维空间场各向异性径向基函数空间插值模型研究[J].测绘学报,2018,47(12):1696.
[102]WU J,ZHANG C,XUE T,et al.Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling[C]//Proceedings of Neural Information Processing Systems.Barcelona:Curran Associates,2016:82-90.
[103]PENG Y X,QI J W,HUANG X.Current Research Status and Prospects on Multimedia Content Understanding[J].Journal of Computer Research and Development,2019,56(1):183-208.(in Chinese)彭宇新,綦金玮,黄鑫.多媒体内容理解的研究现状与展望[J].计算机研究与发展,2019,56(1):183-208.
[104]ODENA A.Semi-Supervised Learning with Generative Adversarial Networks[J].arXiv:1606.01583,2016.
[105]LI H S,WU Y J,ZHENG Y P,et al.A Survey of 3D Data Analysis and Understanding Based on Deep Learning.Chinese Journal of Computers,2019:1-25..http://kns.cnki.net/kcms/detail/11.1826.TP.20190709.1509.002.html.(in Chinese)李海生,武玉娟,郑艳萍,等.基于深度学习的三维数据分 析理解方法研究综述.计算机学报,2019:1-25.[2019-07-25].http://kns.cnki.net/kcms/detail/11.1826.TP.20190709.1509.002.html.
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