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Recent work has shown the ability to learn generative models for 3D shapes from only unstructured 2D images.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
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Towards real-time voxel coloring
Prock, A. and Dyer, C · 1998
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Poxels: Probabilistic voxelized volume reconstruction
De Bonet, J. S. and Viola, P · 1999
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Photorealistic scene reconstruction by voxel coloring
Seitz, S. M. and Dyer, C. R · 1999
Earlier work this paper cites.
A theory of shape by space carving
Kutulakos, K. N. and Seitz, S. M · 2000
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A probabilistic framework for space carving
Broadhurst, A., Drummond, T. W., and Cipolla, R · 2001
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A comparison and evaluation of multi-view stereo reconstruction algorithms
Seitz, S. M., Curless, B., Diebel, J., Scharstein, D., and Szeliski, R · 2006
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ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Patchmatch stereo - stereo matching with slanted support windows
Bleyer, M., Rhemann, C., and Rother, C · 2011
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Fixed-form variational posterior approximation through stochastic linear regression
Salimans, T., Knowles, D. A., et al · 2013
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Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
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Opendr: An approximate differentiable renderer
Loper, M. M. and Black, M. J · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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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., et al · 2015
Earlier work this paper cites.
Massively parallel multiview stereopsis by surface normal diffusion
Galliani, S., Lasinger, K., and Schindler, K · 2015
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Generative and discriminative voxel modeling with convolutional neural networks
Brock, A., Lim, T., Ritchie, J. M., and Weston, N · 2016
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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., and Savarese, S · 2016
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2016
Earlier work this paper cites.
Unsupervised learning of 3d structure from images
Rezende, D. J., Eslami, S. A., Mohamed, S., Battaglia, P., Jaderberg, M., and Heess, N · 2016
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Pixelwise view selection for unstructured multi-view stereo
Schönberger, J. L., Zheng, E., Frahm, J.-M., and Pollefeys, M · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 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, B., and Tenenbaum, J · 2016
Cited alongside, same era.
A point set generation network for 3d object reconstruction from a single image
Fan, H., Su, H., and Guibas, L. J · 2017
Cited alongside, same era.
3d shape induction from 2d views of multiple objects
Gadelha, M., Maji, S., and Wang, R · 2017
Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Cited alongside, same era.
Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2017
Cited alongside, same era.
The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W · 2017
Controlling neural level sets
Atzmon, M., Haim, N., Yariv, L., Israelov, O., Maron, H., and Lipman, Y · 2019
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Large scale GAN training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2019
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Learning implicit fields for generative shape modeling
Chen, Z. and Zhang, H · 2019
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Learning shape templates with structured implicit functions
Genova, K., Cole, F., Vlasic, D., Sarna, A., Freeman, W. T., and Funkhouser, T · 2019
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Learning single-image 3d reconstruction by generative modelling of shape, pose and shading
Henderson, P. and Ferrari, V · 2019
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Escaping plato’s cave: 3d shape from adversarial rendering
Henzler, P., Mitra, N. J., and Ritschel, T · 2019
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Cited alongside, same era.
Octnet: Learning deep 3d representations at high resolutions
Riegler, G., Osman Ulusoy, A., and Geiger, A · 2017
Cited alongside, same era.
Rebar: Low-variance, unbiased gradient estimates for discrete latent variable models
Tucker, G., Mnih, A., Maddison, C. J., Lawson, J., and Sohl-Dickstein, J · 2017
Cited alongside, same era.
Learning representations and generative models for 3d point clouds
Achlioptas, P., Diamanti, O., Mitliagkas, I., and Guibas, L · 2018
Cited alongside, same era.
Neural scene representation and rendering
Eslami, S. A., Rezende, D. J., Besse, F., Viola, F., Morcos, A. S., Garnelo, M., Ruderman, A., Rusu, A. A., Danihelka, I., Gregor, K., et al · 2018
Cited alongside, same era.
Unsupervised training for 3d morphable model regression
Genova, K., Cole, F., Maschinot, A., Sarna, A., Vlasic, D., and Freeman, W. T · 2018
Cited alongside, same era.
A papier-mâché approach to learning 3d surface generation
Groueix, T., Fisher, M., Kim, V. G., Russell, B. C., and Aubry, M · 2018
Cited alongside, same era.
A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
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Towards unsupervised learning of generative models for 3d controllable image synthesis
Liao, Y., Schwarz, K., Mescheder, L., and Geiger, A · 2019
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Learning to infer implicit surfaces without 3d supervision
Liu, S., Saito, S., Chen, W., and Li, H · 2019
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Occupancy networks: Learning 3d reconstruction in function space
Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., and Geiger, A · 2019
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Implicit surface representations as layers in neural networks
Michalkiewicz, M., Pontes, J. K., Jack, D., Baktashmotlagh, M., and Eriksson, A · 2019
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Hologan: Unsupervised learning of 3d representations from natural images
Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., and Yang, Y.-L · 2019
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Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
Niemeyer, M., Mescheder, L., Oechsle, M., and Geiger, A · 2019
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Deep mesh reconstruction from single rgb images via topology modification networks
Pan, J., Han, X., Chen, W., Tang, J., and Jia, K · 2019
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Deepsdf: Learning continuous signed distance functions for shape representation
Park, J. J., Florence, P., Straub, J., Newcombe, R., and Lovegrove, S · 2019
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Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization
Saito, S., Huang, Z., Natsume, R., Morishima, S., Kanazawa, A., and Li, H · 2019
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Scene representation networks: Continuous 3d-structure-aware neural scene representations
Sitzmann, V., Zollhöfer, M., and Wetzstein, G · 2019
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Pix2vox: Context-aware 3d reconstruction from single and multi-view images
Xie, H., Yao, H., Sun, X., Zhou, S., and Zhang, S · 2019
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Disn: Deep implicit surface network for high-quality single-view 3d reconstruction
Xu, Q., Wang, W., Ceylan, D., Mech, R., and Neumann, U · 2019
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Pointflow: 3d point cloud generation with continuous normalizing flows
Yang, G., Huang, X., Hao, Z., Liu, M.-Y., Belongie, S., and Hariharan, B · 2019
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