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Representing 3D shape in deep learning frameworks in an accurate, efficient and compact manner still remains an open challenge.
Learning hierarchical features for scene labeling
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: · 1929
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Free-form deformation of solid geometric models
Sederberg, T., Parry, S.: · 1986
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The earth mover’s distance as a metric for image retrieval
Rubner, Y., Tomasi, C., Guibas, L.J.: · 2000
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A novel connectionist system for unconstrained handwriting recognition
Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., Schmidhuber, J.: · 2009
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: · 2009
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., Hinton, G.E.: · 2012
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Single-view reconstruction via joint analysis of image and shape collections
Huang, Q., Wang, H., Koltun, V.: · 2015
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Towards probabilistic volumetric reconstruction using ray potential
Ulusoy, A.O., Geiger, A., Black, M.J.: · 2015
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3D ShapeNets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Tang, X., Xiao, J.: · 2015
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ShapeNet: An Information-Rich 3D Model Repository
Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., Xiao, J., Yi, L., Yu, F.: · 2015
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3D-R2N2: A unified approach for single and multi-view 3D object reconstruction
Choy, C.B., Xu, D., Gwak, J., Chen, K., Savarese, S.: · 2016
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Perspective transformer nets: Learning single-view 3D object reconstruction without 3D supervision
Yan, X., Yang, J., Yumer, E., Guo, Y., Lee, H.: · 2016
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Volumetric and multi-view CNNs for object classification on 3D data
Qi, C.R., Su, H., Nießner, M., Dai, A., Yan, M., Guibas, L.J.: · 2016
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Multi-label semantic 3D reconstruction using voxel blocks
Cherabier, I., Häne, C., Oswald, M.R., Pollefeys, M.: · 2016
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VConv-DAE: Deep volumetric shape learning without object labels
Sharma, A., Grau, O., Fritz, M.: · 2016
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Unsupervised learning of 3D structure from images
J. Rezende, D., Eslami, S.M.A., Mohamed, S., Battaglia, P., Jaderberg, M., Heess, N.: · 2016
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Learning a predictable and generative vector representation for objects
Girdhar, R., Fouhey, D.F., Rodriguez, M., Gupta, A.: · 2016
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Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling
Wu, J., Zhang, C., Xue, T., Freeman, W.T., Tenenbaum, J.B.: · 2016
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Learning semantic deformation flows with 3D convolutional networks
Yumer, M.E., Mitra, N.J.: · 2016
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Soft 3D reconstruction for view synthesis
Penner, E., Zhang, L.: · 2017
Interactive 3D modeling with a generative adversarial network
Liu, J., Yu, F., Funkhouser, T.A.: · 2017
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Weakly supervised generative adversarial networks for 3D reconstruction
Gwak, J., Choy, C.B., Garg, A., Chandraker, M., Savarese, S.: · 2017
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OctNet: Learning deep 3D representations at high resolutions
Riegler, G., Ulusoy, A.O., Geiger, A.: · 2017
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O-CNN: Octree-based convolutional neural networks for 3D shape analysis
Wang, P.S., Liu, Y., Guo, Y.X., Sun, C.Y., Tong, X.: · 2017
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Hierarchical surface prediction for 3D object reconstruction
Häne, C., Tulsiani, S., Malik, J.: · 2017
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Octree generating networks: Efficient convolutional architectures for high-resolution 3D outputs
Tatarchenko, M., Dosovitskiy, A., Brox, T.: · 2017
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Intrinsic3D: High-quality 3D reconstruction by joint appearance and geometry optimization with spatially-varying lighting
Maier, R., Kim, K., Cremers, D., Kautz, J., Nießner, M.: · 2017
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Learning a multi-view stereo machine
Kar, A., Häne, C., Malik, J.: · 2017
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Rethinking reprojection: Closing the loop for pose-aware shape reconstruction from a single image
Zhu, R., Galoogahi, H.K., Wang, C., Lucey, S.: · 2017
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MarrNet: 3D Shape Reconstruction via 2.5D Sketches
Wu, J., Wang, Y., Xue, T., Sun, X., Freeman, W.T., Tenenbaum, J.B.: · 2017
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A point set generation network for 3D object reconstruction from a single image
Fan, H., Su, H., Guibas, L.J.: · 2017
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PointNet: Deep learning on point sets for 3D classification and segmentation
Qi, C.R., Su, H., Mo, K., Guibas, L.J.: · 2017
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3D shape reconstruction from sketches via multi-view convolutional networks
Lun, Z., Gadelha, M., Kalogerakis, E., Maji, S., Wang, R.: · 2017
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SurfNet: Generating 3D shape surfaces using deep residual network
Sinha, A., Unmesh, A., Huang, Q., Ramani, K.: · 2017
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Using locally corresponding CAD models for dense 3D reconstructions from a single image
Kong, C., Lin, C.H., Lucey, S.: · 2017
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Compact model representation for 3D reconstruction
Pontes, J.K., Kong, C., Eriksson, A., Fookes, C., Sridharan, S., Lucey, S.: · 2017
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DeformNet: Free-form deformation network for 3d shape reconstruction from a single image
Kurenkov, A., Ji, J., Garg, A., Mehta, V., Gwak, J., Choy, C.B., Savarese, S.: · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., Adam, H.: · 2017
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Learning efficient point cloud generation for dense 3D object reconstruction
Lin, C.H., Kong, C., Lucey, S.: · 2018
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