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  • phd文献阅读日志-博一上学期

    为了记住并提醒自己阅读文献,进行了记录(这些论文都是我看过理解的),论文一直在更新中。
    博一上学期:
    1.week 6,2017.10.16
    2014-Automatic Semantic Modeling of Indoor Scenes from Low-quality RGB-D Data using Contextual
    Tsinghua University, Cardiff University(清华大学,英国卡迪夫大学)
    期刊来源:ACM Transaction on Graphic
    2.week 7,2017.10.9
    2014-Annotating RGBD images of indoor scene
    期刊来源:SIGGRAPH Asia 2014 Indoor Scene Understanding Where Graphics Meets Vision. ACM
    3.week 8,2017.10.23
    2016-Discovering overlooked objects: Context-based boosting of object detection in indoor scene
    期刊来源:Pattern recognition letter
    4.week 9,2017.10.30
    2016-FuseNet Incorporating Depth into Semantic Segmentation via Fusion-based CNN Architecture
    期刊来源:Asian Conference on Computer Vision , 2016 :213-228
    5.week10, 2017.11.8
    2015-3D ShapeNets A Deep Representation for Volumetric Shape Modeling
    Princeton University ,Chinese University of Hong Kong, Massachusetts Institute of Technology(普林斯顿大学,香港中文大学,麻省理工学院)
    期刊来源:Wu Z, Song S, Khosla A, et al. 3d shapenets: A deep representation for volumetric shapes[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015: 1912-1920.
    6.week 12, 2017.11.20
    2016-A Point Set Generation Network for 3D Object Reconstruction from a Single Image
    Tsinghua University,Stanford University(清华大学,斯坦福大学)
    期刊来源:Fan H, Su H, Guibas L. A point set generation network for 3d object reconstruction from a single image[J].cvpr,2017.
    7.week 13,16, 2017.11.27,2017.12.18
    2016-Unsupervised 3D Local Feature Learning by Circle Convolutional Restricted Boltzmann Machine
    Northwestern Polytechnical University(西北工业大学)
    期刊来源:Han Z, Liu Z, Han J, et al. Unsupervised 3d local feature learning by circle convolutional restricted boltzmann machine[J]. IEEE Transactions on Image Processing, 2016, 25(11): 5331-5344.
    8.week 17, 2017.12.25
    2017-Perspective Transformer Nets_ Learning Single-View 3D Object Reconstruction without 3D Supervise
    University of Michigan, Ann Arbor, Adobe Research, Google Brain(美国密歇根大学安阿伯分校,Adobe Research,Google大脑)
    期刊来源:Yan X, Yang J, Yumer E, et al. Perspective transformer nets: Learning single-view 3d object reconstruction without 3d supervision[C]//Advances in Neural Information Processing Systems. 2016: 1696-1704.
    9.week18,2018.1.3
    2016-Spatial Transformer Network
    Google DeepMind, London, UK
    期刊来源:Jaderberg M, Simonyan K, Zisserman A. Spatial transformer networks[C]//Advances in Neural Information Processing Systems. 2015: 2017-2025.
    文章理解:http://download.csdn.net/my
    10.week19,2018.1.8
    2017-Using Locally Corresponding CAD Models for Dense 3D Reconstructions from a Single Image
    Carnegie Mellon University(美国卡内基·梅隆大学)
    期刊来源:Kong C, Lin C H, Lucey S. Using Locally Corresponding CAD Models for Dense 3D Reconstructions from a Single Image[C]// IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2017:5603-5611.
    2017-Compact Model Representation for 3D Reconstruction
    Carnegie Mellon University, Queensland University of Technology(美国卡内基·梅隆大学,澳洲昆士兰科技大学)
    期刊来源:Pontes J K, Kong C, Eriksson A, et al. Compact Model Representation for 3D Reconstruction[J]. 3DV,2017.
    11.week20,2018.1.15
    2017-Image2Mesh A Learning Framework for Single Image 3D Reconstruction
    Queensland University of Technologyy, Carnegie Mellon University(澳洲昆士兰科技大学,美国卡内基·梅隆大学)
    期刊来源:Pontes J K, Kong C, Sridharan S, et al. Image2Mesh: A Learning Framework for Single Image 3D Reconstruction[J]. 2017.
    12.week21,2018.1.22
    2018-Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction
    Carnegie Mellon University
    期刊来源:Lin C H, Kong C, Lucey S. Learning efficient point cloud generation for dense 3D object reconstruction[J]. AAAI, 2018.
    13.week22,2018.1.29
    2016-Multi-view 3D Models from Single Images with a Convolutional Network
    University of Freiburg(德国弗赖堡大学)
    期刊来源:Tatarchenko M, Dosovitskiy A, Brox T. Multi-view 3d models from single images with a convolutional network[C]//European Conference on Computer Vision. Springer, Cham, 2016: 322-337.
    2015-Deep convolutional inverse graphics network
    Computer Science and Artificial Intelligence Laboratory, MIT(麻省理工学院,计算机科学与人工智能实验室)
    Brain and Cognitive Sciences, MIT(麻省理工学院,脑和认知科学)
    Microsoft Research Cambridge, UK(英国剑桥,微软研究院)
    期刊来源:Kulkarni T D, Whitney W F, Kohli P, et al. Deep convolutional inverse graphics network[C]//Advances in Neural Information Processing Systems. 2015: 2539-2547.
     
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  • 原文地址:https://www.cnblogs.com/chamie/p/8301565.html
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