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.
