用户名: 密码: 验证码:
深度学习在基于单幅图像的物体三维重建中的应用
详细信息    查看全文 | 推荐本文 |
  • 英文篇名:Application of Deep Learning to 3D Object Reconstruction From a Single Image
  • 作者:陈加 ; 张玉麒 ; 宋鹏 ; 魏艳涛 ; 王煜
  • 英文作者:CHEN Jia;ZHANG Yu-Qi;SONG Peng;WEI Yan-Tao;WANG Yu;School of Educational Information Technology, Central China Normal University;Centre for Vision, Speech and Signal Processing, University of Surrey;Computer Graphics and Geometry Laboratory,■cole Polytechnique F■d■rale de Lausanne;Robotics Institute, the Hong Kong University of Science and Technology;
  • 关键词:三维重建 ; 深度学习 ; 计算机视觉 ; 单幅图像
  • 英文关键词:3D reconstruction;;deep learning;;computer vision;;single image
  • 中文刊名:MOTO
  • 英文刊名:Acta Automatica Sinica
  • 机构:华中师范大学教育信息技术学院;英国萨里大学视觉语音和信号处理中心;瑞士联邦理工学院(洛桑)计算机图形学与几何实验室;香港科技大学机器人研究院;
  • 出版日期:2018-11-28 09:50
  • 出版单位:自动化学报
  • 年:2019
  • 期:v.45
  • 基金:国家自然科学基金(61605054,61502195);; 湖北省自然科学基金(2014CFB659);; 华中师范大学中央高校基本科研业务费(CCNU19QD007,CCNU19TD007,CCNU16JYKX039,CCNU15A05023)资助~~
  • 语种:中文;
  • 页:MOTO201904002
  • 页数:12
  • CN:04
  • ISSN:11-2109/TP
  • 分类号:23-34
摘要
基于单幅图像的物体三维重建是计算机视觉领域的一个重要问题,近几十年来得到了广泛的关注.随着深度学习的不断发展,近年来基于单幅图像的物体三维重建取得了显著进展.本文对深度学习在基于单幅图像的物体三维重建领域的研究进展及具体应用进行了综述.首先介绍了基于单幅图像的三维重建的研究背景及其传统方法的研究现状,其次简要介绍了深度学习并详细综述了深度学习在基于单幅图像的物体三维重建中的应用,随后简要概述了三维物体重建的常用公共数据集,最后进行了分析与总结,指出了目前存在的问题及未来的研究方向.
        3D object reconstruction from a single image is an important topic in computer vision, which has attracted enormous attention during the past decades. With the further study in deep learning, remarkable progress of 3D object reconstruction from a single image has been obtained in recent years. In this paper, we review the applications of deep learning models in the field of 3D object reconstruction from a single image. First, we introduce the research background and the current state-of-the-art of traditional methods. Then, we provide a brief overview of typical deep learning models and we describe the applications of deep learning techniques in 3D object reconstruction from a single image. After that,we list several commonly used data sets for 3D object reconstruction. Finally, we discuss current challenges and further research directions.
引文
1 Rezende D J, Ali Eslami S M, Mohamed S, Battaglia P,Jaderberg M, Heess N. Unsupervised learning of 3D structure from images. In:Proceedings of the 30th Conference on Neural Information Processing Systems(NIPS 2016).New York, USA:Curran Associates, Inc., 2016. 4996-5004
    2 Haming K, Peters G. The structure-from-motion reconstruction pipeline—a survey with focus on short image sequences. Kybernetika, 2010, 46(5):926-937
    3 Lhuillier M, Quan L. A quasi-dense approach to surface reconstruction from uncalibrated images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005,27(3):418-433
    4 Habbecke M, Kobbelt L. A surface-growing approach to multi-view stereo reconstruction. In:Proceedings of the2007 IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis, MN, USA:IEEE, 2007. 1-8
    5 Oswald M R, T¨oppe E, Nieuwenhuis C, Cremers D. A review of geometry recovery from a single image focusing on curved object reconstruction. Innovations for Shape Analysis. Berlin, Germany:Springer-Verlag, 2013. 343-378
    6 Yi L, Shao L, Savva M, Huang H B, Zhou Y, Wang Q R, et al. Large-scale 3D shape reconstruction and segmentation from Shape Net Core55. ar Xiv preprint ar Xiv:1710.06104,2017.
    7 Aspert N, Santa-Cruz D, Ebrahimi T. MESH:measuring errors between surfaces using the Hausdorff distance. In:Proceedings of the 2002 IEEE International Conference on Multimedia and Expo. Lausanne, Switzerland:IEEE,2002. 705-708
    8 Choy C B, Xu D F, Gwak J Y, Chen K, Savarese S. 3DR2N2:a unified approach for single and multi-view 3D object reconstruction. In:Proceedings of the 14th European Conference on Computer Vision. Amsterdam, the Netherlands:Springer, 2016. 628-644
    9 Fan H Q, Su H, Guibas L. A point set generation network for 3D object reconstruction from a single image. In:Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, Hawaii, USA:IEEE,2017. 2463-2471
    10 Kemelmacher-Shlizerman I, Basri R. 3D face reconstruction from a single image using a single reference face shape.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(2):394-405
    11 Wang H K, Stout D B, Chatziioannou A F. Mouse atlas registration with non-tomographic imaging modalitiesa pilot study based on simulation. Molecular Imaging and Biology, 2012, 14(4):408-419
    12 Dworzak J, Lamecker H, Von Berg J, Klinder T, Lorenz C,Kainmu¨ller D, et al. 3D reconstruction of the human rib cage from 2D projection images using a statistical shape model. International Journal of Computer Assisted Radiology and Surgery, 2010, 5(2):111-124
    13 Baka N, Kaptein B L, De Bruijne M, Van Walsum T,Giphart J E, Niessen W J, et al. 2D-3D shape reconstruction of the distal femur from stereo X-ray imaging using statistical shape models. Medical Image Analysis, 2011,15(6):840-850
    14 Blanz V, Vetter T. A morphable model for the synthesis of3 D faces. In:Proceedings of the 26th Annual Conference on Computer Graphics and Interactive Techniques. New York, USA:ACM Press, 1999. 187-194
    15 Cashman T J, Fitzgibbon A W. What shape are dolphins? Building 3D morphable models from 2D images.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1):232-244
    16 Bakshi S, Yang Y H. Shape from shading for nonLambertian surfaces. In:Proceedings of the 1st International Conference on Image Processing. Austin, TX, USA:IEEE, 1994. 130-134
    17 Ahmed A, Farag A. Shape from shading for hybrid surfaces. In:Proceedings of the 2007 IEEE International Conference on Image Processing. San Antonio, TX, USA:IEEE, 2007. II-525-II-528
    18 Jin H L, Soatto S, Yezzi A J. Multi-view stereo reconstruction of dense shape and complex appearance. International Journal of Computer Vision, 2005, 63(3):175-189
    19 Vicente S, Carreira J, Agapito L, Batista J. Reconstructing PASCAL VOC. In:Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition. Columbus, OH, USA:IEEE, 2014. 41-48
    20 Kar A, Tulsiani S, Carreira J, Malik J. Category-specific object reconstruction from a single image. In:Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Boston, MA, USA:IEEE, 2015.1966-1974
    21 Prasad M, Zisserman A, Fitzgibbon A W. Fast and controllable 3D modelling from silhouettes. In:Proceedings of the 2005 Eurographics. Hamburg, Federal Republic of Germany:Elsevier Science Publishing Company, 2005. 9-12
    22 Ikeuchi K, Horn B K P. Numerical shape from shading and occluding boundaries. Artificial Intelligence, 1981,17(1-3):141-184
    23 Prasad M, Fitzgibbon A. Single view reconstruction of curved surfaces. In:Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR 06). New York, NY, USA:IEEE,2006. 1345-1354
    24 Daum M, Dudek G. On 3-D surface reconstruction using shape from shadows. In:Proceedings of the 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Santa Barbara, CA, USA:IEEE,1998. 461-468
    25 Kato H, Ushiku Y, Harada T. Neural 3D mesh renderer. In:Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA:IEEE,2018. 37-44
    26 Rother D, Sapiro G. Seeing 3D objects in a single 2D image. In:Proceedings of the 12th IEEE International Conference on Computer Vision. Kyoto, Japan:IEEE, 2009.1819-1826
    27 Nevatia R, Binford T O. Description and recognition of curved objects. Artificial Intelligence, 1977, 8(1):77-98
    28 Gupta A, Efros A A, Hebert M. Blocks world revisited:image understanding using qualitative geometry and mechanics. In:Proceedings of the 11th European Conference on Computer Vision. Heraklion, Crete, Greece:SpringerVerlag, 2010. 482-496
    29 Xiao J X, Russell B C, Torralba A. Localizing 3D cuboids in single-view images. In:Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada, USA:Curran Associates Inc.,2012. 746-754
    30 Pentland A P. Perceptual organization and the representation of natural form. Artificial Intelligence, 1986, 28(3):293-331
    31 Haag M, Nagel H H. Combination of edge element and optical flow estimates for 3D-model-based vehicle tracking in traffic image sequences. International Journal of Computer Vision, 1999, 35(3):295-319
    32 Koller D, Daniilidis K, Nagel H H. Model-based object tracking in monocular image sequences of road traffic scenes. International Journal of Computer Vision, 1993,10(3):257-281
    33 Lim J J, Pirsiavash H, Torralba A. Parsing Ikea objects:fine pose estimation. In:Proceedings of the 2013 IEEE International Conference on Computer Vision. Sydney,NSW, Australia:IEEE, 2013. 2992-2999
    34 Satkin S, Rashid M, Lin J, Hebert M. 3DNN:3D nearest neighbor. International Journal of Computer Vision, 2015,111(1):69-97
    35 Pepik B, Stark M, Gehler P, Ritschel T, Schiele B. 3D object class detection in the wild. In:Proceedings of the 2015IEEE Conference on Computer Vision and Pattern Recognition Workshops(CVPRW). Boston, MA, USA:IEEE,2015. 1-10
    36 Huang Q X, Wang H, Koltun V. Single-view reconstruction via joint analysis of image and shape collections. ACM Transactions on Graphics(TOG), 2015, 34(4):Article No.87
    37 Liu F, Zeng D, Li J, Zhao Q J. Cascaded regressor based3 D face reconstruction from a single arbitrary view image.[Online], available:https://arxiv.org/abs/1509.06161v1,March 25, 2019
    38 Blanz V, Vetter T. Face recognition based on fitting a 3D morphable model. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(9):1063-1074
    39 Twarog N R, Tappen M F, Adelson E H. Playing with puffball:simple scale-invariant inflation for use in vision and graphics. In:Proceedings of the 2012 ACM Symposium on Applied Perception. Los Angeles, California, USA:ACM,2012. 47-54
    40 Aloimonos J. Shape from texture. Biological Cybernetics,1988, 58(5):345-360
    41 Marinos C, Blake A. Shape from texture:the homogeneity hypothesis. In:Proceedings of the 3rd International Conference on Computer Vision. Osaka, Japan:IEEE, 1990.350-353
    42 Loh A M, Hartley R I. Shape from non-homogeneous, nonstationary, anisotropic, perspective texture. In:Proceedings of the 2005 British Machine Vision Conference. Oxford, UK:BMVC, 2005. 69-78
    43 Horn B K P. Obtaining Shape from Shading Information.Cambridge:MIT Press, 1989. 123-171
    44 Robles-Kelly A, Hancock E R. An eigenvector method for shape-from-shading. In:Proceedings of the 12th International Conference on Image Analysis and Processing. Mantova, Italy:IEEE, 2003. 474-479
    45 Cheung W P, Lee C K, Li K C. Direct shape from shading with improved rate of convergence. Pattern Recognition,1997, 30(3):353-365
    46 Yang L, Han J Q. 3D shape reconstruction of medical images using a perspective shape-from-shading method. Measurement Science and Technology, 2008, 19(6):Article No.065502
    47 Tankus A, Kiryati N. Photometric stereo under perspective projection. In:Proceedings of the 10th IEEE International Conference on Computer Vision. Beijing, China:IEEE, 2005. 611-616
    48 Saxena A, Chung S H, Ng A Y. Learning depth from single monocular images. In:Proceedings of the 18th International Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada:MIT Press,2005. 1161-1168
    49 Saxena A, Sun M, Ng A Y. Make3D:learning 3D scene structure from a single still image. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(5):824-840
    50 Delage E, Lee H, Ng A Y. A dynamic Bayesian network model for autonomous 3D reconstruction from a single indoor image. In:Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR 06). New York, USA:IEEE, 2006.2418-2428
    51 Tulsiani S, Kar A, Carreira J, Malik J. Learning categoryspecific deformable 3D models for object reconstruction.IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(4):719-731
    52 Wang Wei, Gao Wei, Zhu Hai, Hu Zhan-Yi. Rapid and robust piecewise planar reconstruction of urban scenes. Acta Automatica Sinica, 2017, 43(4):674-684(王伟,高伟,朱海,胡占义.快速鲁棒的城市场景分段平面重建.自动化学报, 2017, 43(4):674-684)
    53 Miao Jun, Chu Jun, Zhang Gui-Mei, Wang Lu. Dense multi-planar scene reconstruction from sparse point cloud.Acta Automatica Sinica, 2015, 41(4):813-822(缪君,储珺,张桂梅,王璐.基于稀疏点云的多平面场景稠密重建.自动化学报, 2015, 41(4):813-822)
    54 Zhang Feng, Shi Li-Min, Sun Feng-Mei, Hu Zhan-Yi. An image based 3D reconstruction system for large indoor scenes. Acta Automatica Sinica, 2010, 36(5):625-633(张峰,史利民,孙凤梅,胡占义.一种基于图像的室内大场景自动三维重建系统.自动化学报, 2010, 36(5):625-633)
    55 Le Cun Y, Bengio Y, Hinton G. Deep learning. Nature,2015, 521(7553):436-444
    56 Rumelhart D E, Hinton G E, Williams R J. Learning representations by back-propagating errors. Nature, 1986,323(6088):533-536
    57 Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks. Science, 2006,313(5786):504-507
    58 Jiao Li-Cheng, Yang Shu-Yuan, Liu Fang, Wang Shi-Gang,Feng Zhi-Xi. Seventy years beyond neural networks:retrospect and prospect. Chinese Journal of Computers, 2016,39(8):1697-1716(焦李成,杨淑媛,刘芳,王士刚,冯志玺.神经网络七十年:回顾与展望.计算机学报, 2016, 39(8):1697-1716)
    59 Feng X, Zhang Y D, Glass J. Speech feature denoising and dereverberation via deep autoencoders for noisy reverberant speech recognition. In:Proceedings of the 2014 IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP). Florence, Italy:IEEE, 2014. 1759-1763
    60 Graves A, Mohamed A R, Hinton G. Speech recognition with deep recurrent neural networks. In:Proceedings of the 2013 IEEE International Conference on Acoustics,Speech and Signal Processing. Vancouver, BC, Canada:IEEE, 2013. 6645-6649
    61 Collobert R, Weston J. A unified architecture for natural language processing:deep neural networks with multitask learning. In:Proceedings of the 25th International Conference on Machine Learning. Helsinki, Finland:ACM, 2008.160-167
    62 Huang E H, Socher R, Manning C D, Ng A Y. Improving word representations via global context and multiple word prototypes. In:Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics. Jeju Island, Korea:Association for Computational Linguistics,2012. 873-882
    63 Mikolov T, Chen K, Corrado G S, Dean J. Efficient estimation of word representations in vector space.[Online],available:http://www.oalib.com/paper/4057741, March25, 2019
    64 Krizhevsky A, Sutskever I, Hinton G E. Image Net classification with deep convolutional neural networks. In:Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada, USA:Curran Associates Inc., 2012. 1097-1105
    65 Le Q V. Building high-level features using large scale unsupervised learning. In:Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. Vancouver, BC, Canada:IEEE, 2013. 8595-8598
    66 Socher R, Huval B, Bath B, Manning C D, Ng A Y.Convolutional-recursive deep learning for 3D object classification. In:Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada, USA:Curran Associates Inc., 2012. 656-664
    67 Wu Z R, Song S R, Khosla A, Yu F, Zhang L G, Tang X O, et al. 3D shape Nets:a deep representation for volumetric shapes. In:Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Boston, MA, USA:IEEE, 2015. 1912-1920
    68 Gupta S, Girshick R, Arbel′aez P, Malik J. Learning rich features from RGB-D images for object detection and segmentation. In:Proceedings of the 13th European Conference on Computer Vision. Zurich, Switzerland:SpringerVerlag, 2014. 345-360
    69 Hinton G E, Osindero S, Teh Y W. A fast learning algorithm for deep belief nets. Neural Computation, 2006,18(7):1527-1554
    70 Scholkopf B, Platt J, Hofmann T. Greedy layer-wise training of deep networks. In:Proceedings of the 19th International Conference on Neural Information Processing Systems. Canada:MIT Press, 2006. 153-160
    71 Le Cun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998, 86(11):2278-2324
    72 Williams R J, Zipser D. A learning algorithm for continually running fully recurrent neural networks. Neural Computation, 1989, 1(2):270-280
    73 Girdhar R, Fouhey D F, Rodriguez M, Gupta A. Learning a predictable and generative vector representation for objects. In:Proceedings of the 14th European Conference on Computer Vision. Amsterdam, the Netherlands:SpringerVerlag, 2016. 484-499
    74 Kar A, Hane C, Malik J. Learning a multi-view stereo machine. In:Proceedings of the 31st International Conference on Neural Information Processing Systems(NIPS 2017).New York, USA:Curran Associates, Inc., 2017. 364-375
    75 Wu J J, Wang Y F, Xue T F, Sun X Y, Freeman W T, Tenenbaum J B. Marr Net:3D shape reconstruction via 2.5D sketches. In:Proceedings of the 31st International Conference on Neural Information Processing Systems(NIPS 2017). New York, USA:Curran Associates,Inc., 2017. 8-15
    76 Kanazawa A, Jacobs D W, Chandraker M. Warp Net:weakly supervised matching for single-view reconstruction.In:Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Las Vegas, NV,USA:IEEE, 2016. 3253-3261
    77 Tulsiani S, Zhou T H, Efros A A, Malik J. Multi-view supervision for single-view reconstruction via differentiable ray consistency. In:Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu, Hawaii, USA:IEEE, 2017. 209-217
    78 Tulsiani S. Learning Single-view 3D Reconstruction of Objects and Scenes[Ph. D. dissertation], UC Berkeley, USA,2018
    79 Yan X C, Yang J M, Yumer E, Guo Y J, Lee H. Perspective transformer nets:learning single-view 3D object reconstruction without 3D supervision. In:Proceedings of the 30th Conference on Neural Information Processing Systems(NIPS 2016). New York, USA:Curran Associates,Inc., 2016. 1696-1704
    80 Gwak J Y, Choy C B, Garg A, Chandraker M, Savarese S.Weakly supervised generative adversarial networks for 3D reconstruction. ar Xiv preprint ar Xiv:1705.10904, 2017.263-272
    81 Rosca M, Lakshminarayanan B, Warde-Farley D, Mohamed S. Variational approaches for auto-encoding generative adversarial networks. ar Xiv preprint ar Xiv:1706.04987, 2017.
    82 Zhu R, Galoogahi H K, Wang C Y, Lucey S. Rethinking reprojection:closing the loop for pose-aware shape reconstruction from a single image. In:Proceedings of the2017 IEEE International Conference on Computer Vision(ICCV). Venice, Italy:IEEE, 2017. 57-65
    83 Liu J, Yu F, Funkhouser T. Interactive 3D modeling with a generative adversarial network. In:Proceedings of the2017 International Conference on 3D Vision(3DV). Qingdao, China:IEEE, 2018. 126-134
    84 Wu J J, Zhang C K, Xue T F, Freeman W T, Tenenbaum J B. Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling. In:Proceedings of the 30th Conference on Neural Information Processing Systems(NIPS 2016). New York, USA:Curran Associates,Inc., 2016. 82-90
    85 Gadelha M, Maji S, Wang R. 3D shape induction from2 D views of multiple objects. In:Proceedings of the 2017International Conference on 3D Vision(3DV). Qingdao,China:IEEE, 2017. 402-411
    86 Wang P S, Liu Y, Guo Y X, Sun C Y, Tong X. O-CNN:octree-based convolutional neural networks for 3D shape analysis. ACM Transactions on Graphics(TOG), 2017,36 (4):Article No. 72
    87 Sun Y B, Liu Z W, Wang Y, Sarma S E. Im2avatar:Colorful 3D reconstruction from a single image.[Online], available:https://arxiv.org/abs/1804.06375, March 25, 2019
    88 Tatarchenko M, Dosovitskiy A, Brox T. Octree generating networks:efficient convolutional architectures for highresolution 3D outputs. In:Proceedings of the 2017 IEEE International Conference on Computer Vision(ICCV).Venice, Italy:IEEE, 2017. 2107-2115
    89 Riegler G, Ulusoys A O, Geiger A. Octnet:learning deep3 D representations at high resolutions. In:Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu, Hawaii, USA:IEEE,2017. 6620-6629
    90 Hane C, Tulsiani S, Malik J. Hierarchical surface prediction for 3D object reconstruction. In:Proceedings of the2017 International Conference on 3D Vision(3DV). Qingdao, China:IEEE, 2017. 76-84
    91 Charles R Q, Su H, Mo K, Guibas L J. Point Net:deep learning on point sets for 3D classification and segmentation. In:Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu, Hawaii, USA:IEEE, 2017. 77-85
    92 Qi C R, Yi L, Su H, Guibas L J. Pointnet++:deep hierarchical feature learning on point sets in a metric space. In:Proceedings of the 31st Conference on Neural Information Processing Systems(NIPS 2017). New York, USA:Curran Associates, Inc., 2017. 5099-5108
    93 Klokov R, Lempitsky V. Escape from cells:deep Kdnetworks for the recognition of 3D point cloud models.In:Proceedings of the 2017 IEEE International Conference on Computer Vision(ICCV). Venice, Italy:IEEE,2017. 863-872
    94 Newell A, Yang K Y, Deng J. Stacked hourglass networks for human pose estimation. In:Proceedings of the 14th European Conference on Computer Vision. Amsterdam,the Netherlands:Springer, 2016. 483-499
    95 Lin C H, Kong C, Lucey S. Learning efficient point cloud generation for dense 3D object reconstruction. In:Proceedings of the 31st AAAI Conference on Artificial Intelligence. San Francisco, California, USA:AAAI, 2017. 3-11
    96 Pontes J K, Kong C, Sridharan S, Lucey S, Eriksson A, Fookes C. Image2mesh:A learning framework for single image 3D reconstruction.[Online], available:https://arxiv.org/abs/1711.10669v1, March 25, 2019
    97 Wang N Y, Zhang Y D, Li ZW, Fu Y W, Liu W, Jiang Y G. Pixel2mesh:Generating 3D mesh models from single rgb images.[Online], available:https://arxiv.org/abs/1804.01654v1, March 25, 2019
    98 Xiang Y, Mottaghi R, Savarese S. Beyond PASCAL:a benchmark for 3D object detection in the wild. In:Proceedings of the 2014 IEEE Winter Conference on Applications of Computer Vision. Steamboat Springs, CO, USA:IEEE, 2014. 75-82
    99 Everingham M, Van Gool L, Williams C K I, Winn J,Zisserman A. The PASCAL visual object classes(VOC)challenge. International Journal of Computer Vision, 2010,88(2):303-338
    100 Deng J, Dong W, Socher R, Li L J, Li K, Li F F. Image Net:a large-scale hierarchical image database. In:Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA:IEEE, 2009. 248-255
    101 Chang A X, Funkhouser T, Guibas L, Hanrahan P, Huang Q X, Li Z M, et al. Shapenet:An information-rich 3d model repository.[Online], available:https://arxiv.org/abs/1512.03012v1, March 25, 2019
    102 Miller G A. Word Net:a lexical database for English. Communications of the ACM, 1995, 38(11):39-41
    103 Song H O, Xiang Y, Jegelka S, Savarese S. Deep metric learning via lifted structured feature embedding. In:Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA:IEEE,2016. 4004-4012
    104 Shilane P, Min P, Kazhdan M, Funkhouser T. The princeton shape benchmark. In:Proceedings of the 2004 Shape Modeling Applications. Genova, Italy:IEEE, 2004. 167-178

© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号

地址:北京市海淀区学院路29号 邮编:100083

电话:办公室:(+86 10)66554848;文献借阅、咨询服务、科技查新:66554700