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We present a convolutional network capable of inferring a 3D representation of a previously unseen object given a single image of this object.
3d shapenets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: · 1920
Earlier work this paper cites.
Modeling deep temporal dependencies with recurrent grammar cells””
Michalski, V., Memisevic, R., Konda, K.R.: · 1933
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Category-specific object reconstruction from a single image
Kar, A., Tulsiani, S., Carreira, J., Malik, J.: · 1974
Earlier work this paper cites.
Face recognition based on fitting a 3D morphable model
Blanz, V., Vetter, T.: · 2003
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Histograms of oriented gradients for human detection
Dalal, N., Triggs, B.: · 2005
Earlier work this paper cites.
Unsupervised learning of image transformations
Memisevic, R., Hinton, G.: · 2007
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Visualizing high-dimensional data using t-sne
van der Maaten, L., Hinton, G.: · 2008
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
Lee, H., Grosse, R., Ranganath, R., Ng, A.Y.: · 2009
Earlier work this paper cites.
Transforming auto-encoders
Hinton, G.E., Krizhevsky, A., Wang, S.D.: · 2011
Earlier work this paper cites.
Tgv-fusion
Pock, T., Zebedin, L., Bischof, H.: · 2011
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Fine-grained semi-supervised labeling of large shape collections
Huang, Q.X., Su, H., Guibas, L.: · 2013
Earlier work this paper cites.
Learning to disentangle factors of variation with manifold interaction
Reed, S., Sohn, K., Zhang, Y., Lee, H.: · 2014
Earlier work this paper cites.
Multi-view perceptron: a deep model for learning face identity and view representations
Zhu, Z., Luo, P., Wang, X., Tang, X.: · 2014
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Inferring unseen views of people
Chen, C.Y., Grauman, K.: · 2014
Cited alongside, same era.
Reconstructing PASCAL VOC
Vicente, S., Carreira, J., de Agapito, L., Batista, J.: · 2014
Cited alongside, same era.
Seeing 3d chairs: exemplar part-based 2d-3d alignment using a large dataset of cad models
Aubry, M., Maturana, D., Efros, A., Russell, B., Sivic, J.: · 2014
Cited alongside, same era.
Depth map prediction from a single image using a multi-scale deep network
Eigen, D., Puhrsch, C., Fergus, R.: · 2014
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., Bengio, Y.: · 2014
Cited alongside, same era.
Weakly-supervised disentangling with recurrent transformations for 3d view synthesis
Yang, J., Reed, S.E., Yang, M.H., Lee, H.: · 2015
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Shape, illumination, and reflectance from shading
Barron, J.T., Malik, J.: · 2015
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Cascaded regressor based 3d face reconstruction from a single arbitrary view image
Liu, F., Zeng, D., Li, J., Zhao, Q.: · 2015
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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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Render for cnn: Viewpoint estimation in images using cnns trained with rendered 3d model views
Su, H., Qi, C.R., Li, Y., Guibas, L.J.: · 2015
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TensorFlow: Large-scale machine learning on heterogeneous systems (2015) Software available from tensorflow.org
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Generative adversarial nets
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., Bengio, Y.: · 2014
Cited alongside, same era.
Semantically-Enriched 3D Models for Common-sense Knowledge
Savva, M., Chang, A.X., Hanrahan, P.: · 2015
Cited alongside, same era.
Deep convolutional inverse graphics network
Kulkarni, T.D., Whitney, W.F., Kohli, P., Tenenbaum, J.: · 2015
Cited alongside, same era.
3d-assisted image feature synthesis for novel views of an object
Su, H., Wang, F., Yi, L., Guibas, L.J.: · 2015
Cited alongside, same era.
Learning to generate chairs with convolutional neural networks
A.Dosovitskiy, J.T.Springenberg, T.Brox: · 2015
Cited alongside, same era.
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: · 2015
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Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2015
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K., Zhang, X., Ren, S., Sun, J.: · 2015
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Deep multi-scale video prediction beyond mean square error
Mathieu, M., Couprie, C., LeCun, Y.: · 2015
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Weakly-supervised disentangling with recurrent transformations for 3d view synthesis
Yang, J., Reed, S., Yang, M., Lee, H.: · 2016
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Generating images with perceptual similarity metrics based on deep networks
Dosovitskiy, A., Brox, T.: · 2016
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