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The neural radiance field (NeRF) for realistic novel view synthesis requires camera poses to be pre-acquired by a structure-from-motion (SfM) approach.
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A. Kendall, M. Grimes, and R. Cipolla, “Posenet: A convolutional network for real-time 6-dof camera relocalization,” in Proc. Int. Conf. Comput. Vis., Dec. 2015, pp. 2938-2946
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M. Sundermeyer, Z. Marton, M. Durner, M. Brucker, and R. Triebel, “Implicit 3d orientation learning for 6d object detection from rgb images,” in Proc. Eur. Conf. Comput. Vis., 2018, pp. 699-715
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S. Saito, Z. Huang, R. Natsume, S. Morishima, A. Kanazawa, and H. Li, “Pifu: Pixel-aligned implicit function for high-resolution clothed human digitization,” in Proc. Int. Conf. Comput. Vis., Oct. 2019, pp. 5590-5599
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M. Oechsle, L. Mescheder, M. Niemeyer, T. Strauss, and A. Geiger, “Texture fields: Learning texture representations in function space,” in Proc. Int. Conf. Comput. Vis., Oct. 2019, pp. 5590-5599
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S. Liu, S. Saito, W. Chen, and H. Li, “Learning to infer implicit surfaces without 3d supervision,” in Proc. Adv. Neural Inf. Process. Syst., Dec. 2019, pp. 8295-8306
2019
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L. Liu, J. Gu, K. Lin, T. Chua, and C. Theobalt, “Neural sparse voxel fields,” in Proc. Adv. Neural Inf. Process. Syst., Dec. 2020, pp. 15651-15663
2020
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Z. Wang, S. Wu, W. Xie, M. Chen, and V. A. Prisacariu, “Nerf- -: Neural radiance fields without known camera parameters,” arXiv, 2021
2021
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Q. Meng, A. Chen, H. Luo, M. Wu, H. Su, L. Xu, X. He, and J. Yu, “GNeRF: GAN-based neural radiance field without posed camera,” in Proc. Int. Conf. Comput. Vis., Oct. 2021, pp. 6331-6341
2021
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Y. Wei, S. Liu, Y. Rao, W. Zhao, J. Lu, and J. Zhou, “NerfingMVS: Guided optimization of neural radiance fields for indoor multi-view stereo,” in Proc. Int. Conf. Comput. Vis., Oct. 2021, pp. 5590-5599
2021
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V. Sitzmann, M. Zollh?fer, and G. Wetzstein, “Scene representation networks: continuous 3D-structure-aware neural scene representations,” in Proc. Adv. Neural Inf. Process. Syst., Dec. 2019, pp. 1121-1132
2019
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P. Henzler, N. J. Mitra, and T. Ritschel, “Escaping plato’s cave: 3d shape from adversarial rendering,” in Proc. Int. Conf. Comput. Vis., Oct. 2019, pp. 9983-9992
2019
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T. Nguyen-Phuoc, C. Li, L. Theis, C. Richardt, and Y. L. Yang, “Hologan: Unsupervised learning of 3d representations from natural images,” in Proc. Int. Conf. Comput. Vis., Oct. 2019, pp. 7587-7596
2019
Cited alongside, same era.
S. Peng, Y. Liu, Q. Huang, X. Zhou, and H. Bao, “Pvnet: Pixel-wise voting network for 6dof pose estimation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2019, pp. 4561-4570
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,” ACM Transactions on Graphics, vol. 38, no. 4, pp. 1-14, Aug. 2019
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 Proc. Eur. Conf. Comput. Vis., Sep. 2020, pp. 405-421
2020
Cited alongside, same era.
M. Atzmon and Y. Lipman, “Sal: Sign agnostic learning of shapes from raw data,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2020, pp. 2562-2571
2020
Cited alongside, same era.
A. Gropp, L. Yariv, N. Haim, M. Atzmon, and Y. Lipman, “Implicit Geometric Regularization for Learning Shapes,” in Proc. Int. Conf. Mach. Learn., Jul. 2020, pp. 3789-3799
2020
Cited alongside, same era.
J. T. Barron, B. Mildenhall, M. Tancik, P. Hedman, R. Martin-Brualla, and P. P. Srinivasan, “Mip-NeRF: A multiscale representation for anti-aliasing neural radiance fields,” in Proc. Int. Conf. Comput. Vis., Oct. 2021, pp. 5590-5599
2021
Later among the works it cites.
D. B. Lindell, J. N. P. Martel, and G. Wetzstein, “AutoInt: Automatic integration for fast neural volume rendering,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2021, pp. 14551-14560
2021
Later among the works it cites.
A. Pumarola, E. Corona, G. Pons-Moll, and F. Moreno-Noguer, “D-NeRF: neural radiance fields for dynamic scenes,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2021, pp. 10313-10322
2021
Later among the works it cites.
A. Trevithick and B. Yang, “GRF learning a general radiance field for 3D representation and rendering,” in Proc. Int. Conf. Comput. Vis., Oct. 2021, pp. 15162-15172
2021
Later among the works it cites.
M. Niemeyer and A. Geiger, “Giraffe: Representing scenes as compositional generative neural feature fields,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2021, pp. 11448-11459
2021
Later among the works it cites.
Yu, S. Fridovich-Keil, M. Tancik, Q. Chen, B. Recht, and A. Kanazawa, “Plenoxels: Radiance fields without neural networks,” arXiv, 2021
2021
Later among the works it cites.
L. Chen, P. Florence, J. T. Barron, A. Rodriguez, P. Isola, and T. Lin, “iNeRF: Inverting neural radiance fields for pose estimation,” in International Conference on Intelligent Robots and Systems, Sep. 2021, pp. 1323-1330
2021
Later among the works it cites.
C. Lin, W. Ma, A. Torralba, and S. Lucey, “BARF: Bundle-adjusting neural radiance fields,” in Proc. Int. Conf. Comput. Vis., Oct. 2021, pp. 5721-5731
2021
Later among the works it cites.
G. Riegler and V. Koltun, “Stable View Synthesis,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2021, pp. 12211-12220
2021
Later among the works it cites.
B. Roessle, J. T. Barron, B. Mildenhall, P. P. Srinivasan, and M. Niesner, “Dense depth priors for neural radiance fields from sparse input views,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jul. 2022
2022
Closest in time.
M. Tancik, V. Casser, X. Yan, and S. Pradhan, “Block-NeRF scalable large scene neural view synthesis,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2022
2022
Closest in time.
Q. Xu, Z. Xu, J. Philip, S. Bi, Z. Shu, K. Sunkavalli, and U. Neumann, “Point-NeRF: Point-based neural radiance fields,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2022
2022
Closest in time.
K. Deng, A. Liu, J. Zhu, and D. Ramanan, “Depth-supervised NeRF: Fewer views and faster training for free,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2022
2022
Closest in time.
S. Chen, L. Liang, and J. Ouyang, “Accurate Structure from Motion Using Consistent Cluster Merging,” Multimedia Tool and Applications, 2022, In press
2022
Closest in time.
M. Niemeyer, J. T. Barron, B. Mildenhall, M. S. M. Sajjadi, A. Geiger, and N. Radwan, “RegNeRF: Regularizing neural radiance fields for view synthesis from sparse inputs,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Jun. 2022
2022
Closest in time.
S. Chen, Z. Pu, X. Fan, and B. Zou, “Fixing Defect of Photometric Loss for Self-Supervised Monocular Depth Estimation,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 3, pp. 2043-2055, Mar. 2022
2022
Closest in time.