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Neural Radiance Fields (NeRF), initially developed for static scenes, have inspired many video novel view synthesis techniques.
R. A. Drebin, L. Carpenter, and P. Hanrahan, “Volume rendering,” ACM Siggraph Computer Graphics , vol. 22, no. 4, pp. 65–74, 1988
1988
Earlier work this paper cites.
L. Zhang, B. Curless, and S. M. Seitz, “Spacetime stereo: Shape recovery for dynamic scenes,” in CVPR , vol. 2. IEEE, 2003, pp. II–367
2003
Earlier work this paper cites.
C. L. Zitnick, S. B. Kang, M. Uyttendaele, S. Winder, and R. Szeliski, “High-quality video view interpolation using a layered representation,” ACM transactions on graphics (TOG) , vol. 23, no. 3, pp. 600–608, 2004
2004
Earlier work this paper cites.
J. Telleen, A. Sullivan, J. Yee, O. Wang, P. Gunawardane, I. Collins, and J. Davis, “Synthetic shutter speed imaging,” in Computer Graphics Forum . Wiley Online Library, 2007, pp. 591–598
2007
Earlier work this paper cites.
Q. Shan, J. Jia, and A. Agarwala, “High-quality motion deblurring from a single image,” Acm transactions on graphics (tog) , vol. 27, no. 3, pp. 1–10, 2008
2008
Earlier work this paper cites.
S. Cho and S. Lee, “Fast motion deblurring,” in ACM SIGGRAPH Asia 2009 papers , 2009, pp. 1–8
2009
Earlier work this paper cites.
A. Gupta, N. Joshi, C. L. Zitnick, M. Cohen, and B. Curless, “Single image deblurring using motion density functions,” in ECCV . Springer, 2010, pp. 171–184
2010
Earlier work this paper cites.
S. Harmeling, H. Michael, and B. Schölkopf, “Space-variant single-image blind deconvolution for removing camera shake,” NeurIPS , vol. 23, pp. 829–837, 2010
2010
Earlier work this paper cites.
L. Xu and J. Jia, “Two-phase kernel estimation for robust motion deblurring,” in Computer Vision–ECCV 2010: 11th European Conference on Computer Vision, Heraklion, Crete, Greece, September 5-11, 2010, Proceedings, Part I 11 . Springer, 2010, pp. 157–170
2010
Earlier work this paper cites.
J. Valmadre and S. Lucey, “General trajectory prior for non-rigid reconstruction,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2012, pp. 1394–1401
2012
Earlier work this paper cites.
M. R. Oswald, J. Stühmer, and D. Cremers, “Generalized connectivity constraints for spatio-temporal 3d reconstruction,” in ECCV . Springer, 2014, pp. 32–46
2014
Earlier work this paper cites.
A. Collet, M. Chuang, P. Sweeney, D. Gillett, D. Evseev, D. Calabrese, H. Hoppe, A. Kirk, and S. Sullivan, “High-quality streamable free-viewpoint video,” ACM Transactions on Graphics (ToG) , vol. 34, no. 4, pp. 1–13, 2015
2015
Earlier work this paper cites.
C. J. Schuler, M. Hirsch, S. Harmeling, and B. Schölkopf, “Learning to deblur,” IEEE transactions on pattern analysis and machine intelligence , vol. 38, no. 7, pp. 1439–1451, 2015
2015
Earlier work this paper cites.
J. Sun, W. Cao, Z. Xu, and J. Ponce, “Learning a convolutional neural network for non-uniform motion blur removal,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 769–777
2015
Earlier work this paper cites.
J. Pan, D. Sun, H. Pfister, and M.-H. Yang, “Blind image deblurring using dark channel prior,” in CVPR , 2016, pp. 1628–1636
2016
Earlier work this paper cites.
A. Chakrabarti, “A neural approach to blind motion deblurring,” in Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III 14 . Springer, 2016, pp. 221–235
2016
Earlier work this paper cites.
J. L. Schonberger and J.-M. Frahm, “Structure-from-motion revisited,” in CVPR , 2016, pp. 4104–4113
2016
Earlier work this paper cites.
Y. Bahat, N. Efrat, and M. Irani, “Non-uniform blind deblurring by reblurring,” in ICCV , 2017, pp. 3286–3294
2017
Earlier work this paper cites.
S. Nah, T. Hyun Kim, and K. Mu Lee, “Deep multi-scale convolutional neural network for dynamic scene deblurring,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 3883–3891
2017
Earlier work this paper cites.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in ICCV , 2017, pp. 2961–2969
2017
Earlier work this paper cites.
S. Niklaus, L. Mai, and F. Liu, “Video frame interpolation via adaptive separable convolution,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 261–270
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
K. Zhang, W. Luo, Y. Zhong, L. Ma, W. Liu, and H. Li, “Adversarial spatio-temporal learning for video deblurring,” IEEE Transactions on Image Processing , vol. 28, no. 1, pp. 291–301, 2018
2018
Earlier work this paper cites.
P. Bojanowski, A. Joulin, D. Lopez-Paz, and A. Szlam, “Optimizing the latent space of generative networks,” in Proceedings of the 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm, Sweden, July 10-15, 2018 , 2018
2018
Earlier work this paper cites.
Z. Li and N. Snavely, “Megadepth: Learning single-view depth prediction from internet photos,” in CVPR , 2018
2018
Earlier work this paper cites.
S. Zhou, J. Zhang, J. Pan, H. Xie, W. Zuo, and J. Ren, “Spatio-temporal filter adaptive network for video deblurring,” in ICCV , 2019, pp. 2482–2491
2019
Earlier work this paper cites.
S. Nah, S. Baik, S. Hong, G. Moon, S. Son, R. Timofte, and K. M. Lee, “Ntire 2019 challenge on video deblurring and super-resolution: Dataset and study,” in CVPR Workshops , June 2019
2019
Earlier work this paper cites.
S. Zhou, J. Zhang, W. Zuo, H. Xie, J. Pan, and J. S. Ren, “Davanet: Stereo deblurring with view aggregation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 10 996–11 005
2019
Earlier work this paper cites.
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 ECCV , 2020
2020
Earlier work this paper cites.
M. Broxton, J. Flynn, R. Overbeck, D. Erickson, P. Hedman, M. Duvall, J. Dourgarian, J. Busch, M. Whalen, and P. Debevec, “Immersive light field video with a layered mesh representation,” ACM Transactions on Graphics (TOG) , vol. 39, no. 4, pp. 86–1, 2020
2020
Earlier work this paper cites.
K. Zhang, W. Luo, Y. Zhong, L. Ma, B. Stenger, W. Liu, and H. Li, “Deblurring by realistic blurring,” in CVPR , 2020, pp. 2737–2746
2020
Earlier work this paper cites.
J. Pan, H. Bai, and J. Tang, “Cascaded deep video deblurring using temporal sharpness prior,” in CVPR , 2020, pp. 3043–3051
2020
Earlier work this paper cites.
R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V. Koltun, “Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,” IEEE transactions on pattern analysis and machine intelligence , 2020
2020
Earlier work this paper cites.
Z. Teed and J. Deng, “Raft: Recurrent all-pairs field transforms for optical flow,” in ECCV . Springer, 2020, pp. 402–419
2020
Cited alongside, same era.
W. Shen, W. Bao, G. Zhai, L. Chen, X. Min, and Z. Gao, “Blurry video frame interpolation,” in CVPR , 2020, pp. 5114–5123
2020
Cited alongside, same era.
M. Chu, Y. Xie, J. Mayer, L. Leal-Taixé, and N. Thuerey, “Learning temporal coherence via self-supervision for gan-based video generation,” ACM Transactions on Graphics (TOG) , vol. 39, no. 4, pp. 75–1, 2020
2020
Cited alongside, same era.
A. Pumarola, E. Corona, G. Pons-Moll, and F. Moreno-Noguer, “D-nerf: Neural radiance fields for dynamic scenes,” in CVPR , 2021, pp. 10 318–10 327
2021
Cited alongside, same era.
Z. Li, S. Niklaus, N. Snavely, and O. Wang, “Neural scene flow fields for space-time view synthesis of dynamic scenes,” in CVPR , 2021, pp. 6498–6508
S. Athar, Z. Xu, K. Sunkavalli, E. Shechtman, and Z. Shu, “Rignerf: Fully controllable neural 3d portraits,” in CVPR , 2022
2022
Later among the works it cites.
B. Van Hoorick, P. Tendulkar, D. Suris, D. Park, S. Stent, and C. Vondrick, “Revealing occlusions with 4d neural fields,” in CVPR , 2022, pp. 3011–3021
2022
Later among the works it cites.
A. Chen, Z. Xu, A. Geiger, J. Yu, and H. Su, “Tensorf: Tensorial radiance fields,” in ECCV . Springer, 2022, pp. 333–350
2022
Later among the works it cites.
S. Fridovich-Keil, A. Yu, M. Tancik, Q. Chen, B. Recht, and A. Kanazawa, “Plenoxels: Radiance fields without neural networks,” in CVPR , 2022, pp. 5501–5510
2022
Later among the works it cites.
“Roboflow.” [Online]. Available: https://roboflow.com/
2022
Later among the works it cites.
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2021
Cited alongside, same era.
C. Gao, A. Saraf, J. Kopf, and J.-B. Huang, “Dynamic view synthesis from dynamic monocular video,” in ICCV , 2021, pp. 5712–5721
2021
Cited alongside, same era.
E. Tretschk, A. Tewari, V. Golyanik, M. Zollhöfer, C. Lassner, and C. Theobalt, “Non-rigid neural radiance fields: Reconstruction and novel view synthesis of a dynamic scene from monocular video,” in ICCV , 2021, pp. 12 959–12 970
2021
Cited alongside, same era.
S. Deng, W. Ren, Y. Yan, T. Wang, F. Song, and X. Cao, “Multi-scale separable network for ultra-high-definition video deblurring,” in ICCV , 2021, pp. 14 030–14 039
2021
Cited alongside, same era.
Z. Zhong, Y. Zheng, and I. Sato, “Towards rolling shutter correction and deblurring in dynamic scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 9219–9228
2021
Cited alongside, same era.
K. Park, U. Sinha, P. Hedman, J. T. Barron, S. Bouaziz, D. B. Goldman, R. Martin-Brualla, and S. M. Seitz, “Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields,” ACM Trans. Graph. , vol. 40, no. 6, dec 2021
2021
Cited alongside, same era.
K. Park, U. Sinha, J. T. Barron, S. Bouaziz, D. B. Goldman, S. M. Seitz, and R. Martin-Brualla, “Nerfies: Deformable neural radiance fields,” in ICCV , 2021, pp. 5865–5874
2021
Cited alongside, same era.
W. Xian, J.-B. Huang, J. Kopf, and C. Kim, “Space-time neural irradiance fields for free-viewpoint video,” in CVPR , 2021, pp. 9421–9431
2021
Cited alongside, same era.
Z. Li, Q. Wang, F. Cole, R. Tucker, and N. Snavely, “Dynibar: Neural dynamic image-based rendering,” in CVPR , 2023, pp. 4273–4284
2023
Closest in time.
B. Attal, J.-B. Huang, C. Richardt, M. Zollhoefer, J. Kopf, M. O’Toole, and C. Kim, “Hyperreel: High-fidelity 6-dof video with ray-conditioned sampling,” in CVPR , 2023, pp. 16 610–16 620
2023
Closest in time.
S. Park, M. Son, S. Jang, Y. C. Ahn, J.-Y. Kim, and N. Kang, “Temporal interpolation is all you need for dynamic neural radiance fields,” in CVPR , 2023, pp. 4212–4221
2023
Closest in time.
R. Shao, Z. Zheng, H. Tu, B. Liu, H. Zhang, and Y. Liu, “Tensor4d: Efficient neural 4d decomposition for high-fidelity dynamic reconstruction and rendering,” in CVPR , 2023, pp. 16 632–16 642
2023
Closest in time.
S. Fridovich-Keil, G. Meanti, F. R. Warburg, B. Recht, and A. Kanazawa, “K-planes: Explicit radiance fields in space, time, and appearance,” in CVPR , 2023, pp. 12 479–12 488
2023
Closest in time.
P. Wang, L. Zhao, R. Ma, and P. Liu, “Bad-nerf: Bundle adjusted deblur neural radiance fields,” in CVPR , 2023, pp. 4170–4179
2023
Closest in time.
D. Lee, M. Lee, C. Shin, and S. Lee, “Dp-nerf: Deblurred neural radiance field with physical scene priors,” in CVPR , 2023, pp. 12 386–12 396
2023
Closest in time.
Q. Zhu, M. Zhou, N. Zheng, C. Li, J. Huang, and F. Zhao, “Exploring temporal frequency spectrum in deep video deblurring,” in ICCV , 2023, pp. 12 428–12 437
2023
Closest in time.
J. Pan, B. Xu, J. Dong, J. Ge, and J. Tang, “Deep discriminative spatial and temporal network for efficient video deblurring,” in CVPR , 2023, pp. 22 191–22 200
2023
Closest in time.
D. Li, X. Shi, Y. Zhang, K. C. Cheung, S. See, X. Wang, H. Qin, and H. Li, “A simple baseline for video restoration with grouped spatial-temporal shift,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9822–9832
2023
Closest in time.
Z. Fang, F. Wu, W. Dong, X. Li, J. Wu, and G. Shi, “Self-supervised non-uniform kernel estimation with flow-based motion prior for blind image deblurring,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 18 105–18 114
2023
Closest in time.
D. Lee, J. Oh, J. Rim, S. Cho, and K. M. Lee, “Exblurf: Efficient radiance fields for extreme motion blurred images,” in ICCV , 2023, pp. 17 639–17 648
2023
Closest in time.
Y. Liu, C. Gao, A. Meuleman, H. Tseng, A. Saraf, C. Kim, Y. Chuang, J. Kopf, and J. Huang, “Robust dynamic radiance fields,” in CVPR , 2023, pp. 13–23
2023
Closest in time.
A. Cao and J. Johnson, “Hexplane: A fast representation for dynamic scenes,” in CVPR , 2023, pp. 130–141
2023
Closest in time.
L. Song, A. Chen, Z. Li, Z. Chen, L. Chen, J. Yuan, Y. Xu, and A. Geiger, “Nerfplayer: A streamable dynamic scene representation with decomposed neural radiance fields,” IEEE Transactions on Visualization and Computer Graphics , vol. 29, no. 5, pp. 2732–2742, 2023
2023
Closest in time.
B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering.” ACM Trans. Graph. , vol. 42, no. 4, pp. 139–1, 2023
2023
Closest in time.
Z. Yan, C. Li, and G. H. Lee, “Nerf-ds: Neural radiance fields for dynamic specular objects,” in CVPR , 2023
2023
Closest in time.
2023
Closest in time.
H. Sun, X. Li, L. Shen, X. Ye, K. Xian, and Z. Cao, “Dyblurf: Dynamic neural radiance fields from blurry monocular video,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 7517–7527
2024
Closest in time.
G. Wu, T. Yi, J. Fang, L. Xie, X. Zhang, W. Wei, W. Liu, Q. Tian, and X. Wang, “4d gaussian splatting for real-time dynamic scene rendering,” in CVPR , 2024
2024
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Y. Duan, F. Wei, Q. Dai, Y. He, W. Chen, and B. Chen, “4d-rotor gaussian splatting: towards efficient novel view synthesis for dynamic scenes,” in ACM SIGGRAPH 2024 Conference Papers , 2024, pp. 1–11
2024
Closest in time.
C. Stearns, A. Harley, M. Uy, F. Dubost, F. Tombari, G. Wetzstein, and L. Guibas, “Dynamic gaussian marbles for novel view synthesis of casual monocular videos,” in SIGGRAPH Asia 2024 Conference Papers , 2024, pp. 1–11
2024
Closest in time.
2024
Closest in time.
2024
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2024
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A. Luthra, S. S. Gantha, X. Song, H. Yu, Z. Lin, and L. Peng, “Deblur-nsff: Neural scene flow fields for blurry dynamic scenes,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2024, pp. 3658–3667
2024
Closest in time.
2024
Closest in time.
C. Peng, Y. Tang, Y. Zhou, N. Wang, X. Liu, D. Li, and R. Chellappa, “Bags: Blur agnostic gaussian splatting through multi-scale kernel modeling,” in European Conference on Computer Vision . Springer, 2025, pp. 293–310
2025
Closest in time.