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We propose a framework for learning neural scene representations directly from images, without 3D supervision.
Scene representation networks: Continuous 3d-structure-aware neural scene representations
Sitzmann, V., Zollhöfer, M., and Wetzstein, G · 1906
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A fast algorithm for general raster rotation
Paeth, A · 1986
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A volumetric method for building complex models from range images
Curless, B. and Levoy, M · 1996
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Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., and Simoncelli, E. P · 2004
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Mitsuba renderer, 2010
Jakob, W · 2010
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Computer vision: algorithms and applications
Szeliski, R · 2010
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Kinectfusion: Real-time dense surface mapping and tracking
Newcombe, R. A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A. J., Kohli, P., Shotton, J., Hodges, S., and Fitzgibbon, A. W · 2011
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Real-time 3d reconstruction at scale using voxel hashing
Nießner, M., Zollhöfer, M., Izadi, S., and Stamminger, M · 2013
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Mitsuba for shapenet, 2014
Shi, J · 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
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Voxnet: A 3d convolutional neural network for real-time object recognition
Maturana, D. and Scherer, S · 2015
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Scalable inside-out image-based rendering
Hedman, P., Ritschel, T., Drettakis, G., and Brostow, G · 2016
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Multi-view 3d models from single images with a convolutional network
Tatarchenko, M., Dosovitskiy, A., and Brox, T · 2016
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View synthesis by appearance flow
Zhou, T., Tulsiani, S., Sun, W., Malik, J., and Efros, A. A · 2016
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Learning so(3) equivariant representations with spherical cnns
Esteves, C., Allen-Blanchette, C., Makadia, A., and Daniilidis, K · 2017
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Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R., Su, H., Mo, K., and Guibas, L. J · 2017
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Learning to generate images with perceptual similarity metrics
Snell, J., Ridgeway, K., Liao, R., Roads, B. D., Mozer, M. C., and Zemel, R. S · 2017
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Interpretable transformations with encoder-decoder networks
Worrall, D. E., Garbin, S. J., Turmukhambetov, D., and Brostow, G. J · 2017
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Neural scene representation and rendering
Multi-view to novel view: Synthesizing novel views with self-learned confidence
Sun, S.-H., Huh, M., Liao, Y.-H., Zhang, N., and Lim, J. J · 2018
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Ignor: Image-guided neural object rendering
Thies, J., Zollhöfer, M., Theobalt, C., Stamminger, M., and Nießner, M · 2018
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Group normalization
Wu, Y. and He, K · 2018
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Visual object networks: Image generation with disentangled 3d representations
Zhu, J.-Y., Zhang, Z., Zhang, C., Wu, J., Torralba, A., Tenenbaum, J., and Freeman, B · 2018
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Monocular neural image based rendering with continuous view control
Chen, X., Song, J., and Hilliges, O · 2019
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Hologan: Unsupervised learning of 3d representations from natural images
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Eslami, S. M. A., Jimenez Rezende, D., Besse, F., Viola, F., Morcos, A. S., Garnelo, M., Ruderman, A., Rusu, A. A., Danihelka, I., Gregor, K., Reichert, D. P., Buesing, L., Weber, T., Vinyals, O., Rosenbaum, D., Rabinowitz, N., King, H., Hillier, C., Botvinick, M., Wierstra, D., Kavukcuoglu, K., and Hassabis, D · 2018
Cited alongside, same era.
Deep blending for free-viewpoint image-based rendering
Hedman, P., Philip, J., Price, T., Frahm, J.-M., Drettakis, G., and Brostow, G · 2018
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Unsupervised learning of shape and pose with differentiable point clouds
Insafutdinov, E. and Dosovitskiy, A · 2018
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Learning free-form deformations for 3d object reconstruction
Jack, D., Pontes, J. K., Sridharan, S., Fookes, C., Shirazi, S., Maire, F., and Eriksson, A · 2018
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Group equivariant capsule networks
Lenssen, J. E., Fey, M., and Libuschewski, P · 2018
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Occupancy networks: Learning 3d reconstruction in function space, 2018
Mescheder, L., Oechsle, M., Niemeyer, M., Nowozin, S., and Geiger, A · 2018
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Rendernet: A deep convolutional network for differentiable rendering from 3d shapes
Nguyen-Phuoc, T. H., Li, C., Balaban, S., and Yang, Y · 2018
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Nguyen-Phuoc, T., Li, C., Theis, L., Richardt, C., and Yang, Y.-L · 2019
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Transformable bottleneck networks
Olszewski, K., Tulyakov, S., Woodford, O., Li, H., and Luo, L · 2019
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Deepsdf: Learning continuous signed distance functions for shape representation
Park, J. J., Florence, P., Straub, J., Newcombe, R. A., and Lovegrove, S · 2019
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What do single-view 3d reconstruction networks learn?
Tatarchenko, M., Richter, S. R., Ranftl, R., Li, Z., Koltun, V., and Brox, T · 2019
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Deferred neural rendering: Image synthesis using neural textures
Thies, J., Zollhöfer, M., and Nießner, M · 2019
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Geometry-aware neural rendering
Tobin, J., Zaremba, W., and Abbeel, P · 2019
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Nerf: Representing scenes as neural radiance fields for view synthesis
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., and Ng, R · 2020
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