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We propose a Transformer-based NeRF (TransNeRF) to learn a generic neural radiance field conditioned on observed-view images for the novel view synthesis task.
J. T. Kajiya and B. P. Von Herzen, “Ray tracing volume densities,”
1984
Earlier work this paper cites.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,”
2004
Earlier work this paper cites.
B. Ham, D. Min, C. Oh, M. N. Do, and K. Sohn, “Probability-based rendering for view synthesis,”
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,”
2014
Earlier work this paper cites.
J. L. Schonberger and J.-M. Frahm, “Structure-from-motion revisited,” in
2016
Earlier work this paper cites.
K. Rematas, C. H. Nguyen, T. Ritschel, M. Fritz, and T. Tuytelaars, “Novel views of objects from a single image,”
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in
2017
Earlier work this paper cites.
S. Tulsiani, T. Zhou, A. A. Efros, and J. Malik, “Multi-view supervision for single-view reconstruction via differentiable ray consistency,” in
2017
Earlier work this paper cites.
D. M. M. Rahaman and M. Paul, “Virtual view synthesis for free viewpoint video and multiview video compression using gaussian mixture modelling,”
2018
Earlier work this paper cites.
B. Yang, S. Rosa, A. Markham, N. Trigoni, and H. Wen, “Dense 3d object reconstruction from a single depth view,”
2018
Earlier work this paper cites.
E. Insafutdinov and A. Dosovitskiy, “Unsupervised learning of shape and pose with differentiable point clouds,”
2018
Earlier work this paper cites.
T. Zhou, R. Tucker, J. Flynn, G. Fyffe, and N. Snavely, “Stereo magnification: learning view synthesis using multiplane images,”
2018
Earlier work this paper cites.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in
2018
Earlier work this paper cites.
G. Gkioxari, J. Malik, and J. Johnson, “Mesh r-cnn,” in
2019
Cited alongside, same era.
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger, “Occupancy networks: Learning 3d reconstruction in function space,” in
2019
Cited alongside, same era.
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove, “Deepsdf: Learning continuous signed distance functions for shape representation,” in
2019
Cited alongside, same era.
V. Sitzmann, M. Zollhöfer, and G. Wetzstein, “Scene representation networks: Continuous 3d-structure-aware neural scene representations,”
2019
Cited alongside, same era.
B. Mildenhall, P. P. Srinivasan, R. Ortiz-Cayon, N. K. Kalantari, R. Ramamoorthi, R. Ng, and A. Kar, “Local light field fusion: Practical view synthesis with prescriptive sampling guidelines,”
2019
Cited alongside, same era.
M. Chen, A. Radford, R. Child, J. Wu, H. Jun, D. Luan, and I. Sutskever, “Generative pretraining from pixels,” in
2020
Later among the works it cites.
Q. Wang, Z. Wang, K. Genova, P. P. Srinivasan, H. Zhou, J. T. Barron, R. Martin-Brualla, N. Snavely, and T. Funkhouser, “Ibrnet: Learning multi-view image-based rendering,” in
2021
Later among the works it cites.
N. Meng, K. Li, J. Liu, and E. Y. Lam, “Light field view synthesis via aperture disparity and warping confidence map,”
2021
Later among the works it cites.
L. Liu, M. Habermann, V. Rudnev, K. Sarkar, J. Gu, and C. Theobalt, “Neural actor: Neural free-view synthesis of human actors with pose control,”
2021
Later among the works it cites.
D. Wang, X. Cui, X. Chen, Z. Zou, T. Shi, S. Salcudean, Z. J. Wang, and R. Ward, “Multi-view 3d reconstruction with transformers,” in
2021
Later among the works it cites.
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J. Flynn, M. Broxton, P. E. Debevec, M. DuVall, G. Fyffe, R. S. Overbeck, N. Snavely, and R. Tucker, “Deepview: View synthesis with learned gradient descent.” in
2019
Cited alongside, same era.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” in
2020
Cited alongside, same era.
C. Nash, Y. Ganin, S. A. Eslami, and P. Battaglia, “Polygen: An autoregressive generative model of 3d meshes,” in
2020
Cited alongside, same era.
K. Genova, F. Cole, A. Sud, A. Sarna, and T. Funkhouser, “Local deep implicit functions for 3d shape,” in
2020
Cited alongside, same era.
C. Jiang, A. Sud, A. Makadia, J. Huang, M. Nießner, T. Funkhouser
2020
Cited alongside, same era.
M. Niemeyer, L. Mescheder, M. Oechsle, and A. Geiger, “Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision,” in
2020
Cited alongside, same era.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell
2020
Cited alongside, same era.
A. Yu, V. Ye, M. Tancik, and A. Kanazawa, “pixelnerf: Neural radiance fields from one or few images,” in
2021
Later among the works it cites.
A. Chen, Z. Xu, F. Zhao, X. Zhang, F. Xiang, J. Yu, and H. Su, “Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo,” in
2021
Later among the works it cites.
A. Trevithick and B. Yang, “Grf: Learning a general radiance field for 3d representation and rendering,” in
2021
Later among the works it cites.
R. Martin-Brualla, N. Radwan, M. S. Sajjadi, J. T. Barron, A. Dosovitskiy, and D. Duckworth, “Nerf in the wild: Neural radiance fields for unconstrained photo collections,” in
2021
Later among the works it cites.
A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in
2021
Later among the works it cites.
D. Wang, X. Cui, X. Chen, R. Ward, and Z. J. Wang, “Interpreting bottom-up decision-making of cnns via hierarchical inference,”
2021
Later among the works it cites.
A. Palazzi, L. Bergamini, S. Calderara, and R. Cucchiara, “Warp and learn: Novel views generation for vehicles and other objects,”
2022
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