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Novel view synthesis is an important problem in computer vision and graphics.
The plenoptic function and the elements of early vision
Adelson, E.H., Bergen, J.R., et al.: · 1991
Earlier work this paper cites.
View interpolation for image synthesis
Chen, S.E., Williams, L.: · 1993
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Physically-valid view synthesis by image interpolation
Seitz, S.M., Dyer, C.R.: · 1995
Earlier work this paper cites.
Plenoptic modeling: An image-based rendering system
McMillan, L., Bishop, G.: · 1995
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Stereo vision for view synthesis
Scharstein, D.: · 1996
Earlier work this paper cites.
Light field rendering
Levoy, M., Hanrahan, P.: · 1996
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View morphing
Seitz, S.M., Dyer, C.R.: · 1996
Earlier work this paper cites.
High-quality video view interpolation using a layered representation
Zitnick, C.L., Kang, S.B., Uyttendaele, M., Winder, S., Szeliski, R.: · 2004
Earlier work this paper cites.
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: · 2004
Earlier work this paper cites.
Image-based rendering using image-based priors
Fitzgibbon, A., Wexler, Y., Zisserman, A.: · 2005
Earlier work this paper cites.
Ambient point clouds for view interpolation
Goesele, M., Ackermann, J., Fuhrmann, S., Haubold, C., Klowsky, R., Steedly, D., Szeliski, R.: · 2010
Earlier work this paper cites.
Real-time local stereo matching using guided image filtering
Hosni, A., Bleyer, M., Rhemann, C., Gelautz, M., Rother, C.: · 2011
Earlier work this paper cites.
Are we ready for autonomous driving? the kitti vision benchmark suite
Geiger, A., Lenz, P., Urtasun, R.: · 2012
Earlier work this paper cites.
Depth synthesis and local warps for plausible image-based navigation
Chaurasia, G., Duchene, S., Sorkine-Hornung, O., Drettakis, G.: · 2013
Earlier work this paper cites.
Depth map prediction from a single image using a multi-scale deep network
Eigen, D., Puhrsch, C., Fergus, R.: · 2014
Cited alongside, same era.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J.: · 2014
Cited alongside, same era.
Deepstereo: Learning to predict new views from the world’s imagery
Flynn, J., Neulander, I., Philbin, J., Snavely, N.: · 2015
Cited alongside, same era.
Learning to generate chairs with convolutional neural networks
Dosovitskiy, A., Tobias Springenberg, J., Brox, T.: · 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.
Unsupervised monocular depth estimation with left-right consistency
Godard, C., Mac Aodha, O., Brostow, G.J.: · 2016
Later among the works it cites.
Patchbatch: a batch augmented loss for optical flow
Gadot, D., Wolf, L.: · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., Sun, J.: · 2016
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Soft 3d reconstruction for view synthesis
Penner, E., Zhang, L.: · 2017
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High-resolution image synthesis and semantic manipulation with conditional gans
Wang, T.C., Liu, M.Y., Zhu, J.Y., Tao, A., Kautz, J., Catanzaro, B.: · 2017
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Photographic image synthesis with cascaded refinement networks
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Yang, J., Reed, S.E., Yang, M.H., Lee, H.: · 2015
Cited alongside, same era.
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
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., Brox, T.: · 2015
Cited alongside, same era.
Multi-view 3d models from single images with a convolutional network
Tatarchenko, M., Dosovitskiy, A., Brox, T.: · 2016
Cited alongside, same era.
View synthesis by appearance flow
Zhou, T., Tulsiani, S., Sun, W., Malik, J., Efros, A.A.: · 2016
Cited alongside, same era.
Learning-based view synthesis for light field cameras
Kalantari, N.K., Wang, T.C., Ramamoorthi, R.: · 2016
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: · 2016
Cited alongside, same era.
Chen, Q., Koltun, V.: · 2017
Later among the works it cites.
Unsupervised learning of depth and ego-motion from video
Zhou, T., Brown, M., Snavely, N., Lowe, D.G.: · 2017
Later among the works it cites.
End-to-end learning of geometry and context for deep stereo regression
Kendall, A., Martirosyan, H., Dasgupta, S., Henry, P., Kennedy, R., Bachrach, A., Bry, A.: · 2017
Later among the works it cites.
Self-supervised learning for stereo matching with self-improving ability
Zhong, Y., Dai, Y., Li, H.: · 2017
Later among the works it cites.
Cnn-based patch matching for optical flow with thresholded hinge embedding loss
Bailer, C., Varanasi, K., Stricker, D.: · 2017
Later among the works it cites.
Densely connected convolutional networks
Huang, G., Liu, Z., Weinberger, K.Q., van der Maaten, L.: · 2017
Later among the works it cites.
Geometry-aware deep network for single-image novel view synthesis
Liu, M., He, X., Salzmann, M.: · 2018
Closest in time.
Novel view synthesis for large-scale scene using adversarial loss
Yin, X., Wei, H., Wang, X., Chen, Q., et al.: · 2018
Closest in time.
Spatial transformer networks
Jaderberg, M., Simonyan, K., Zisserman, A., et al.: · 2025
Closest in time.