Fetching the paper…
Reading the bibliography…
Although significant progress has been made in reconstructing sharp 3D scenes from motion-blurred images, a transition to real-world applications remains challenging.
Max, N.: Optical models for direct volume rendering. IEEE Transactions on Visualization and Computer Graphics pp. 99–108 (1995). https://doi.org/10.1109/2945.468400
1995
Earlier work this paper cites.
Zwicker, M., Pfister, H., Baar, J.V., Gross, M.: Surface splatting. Proceedings of the 28th annual conference on Computer graphics and interactive techniques (2001)
2001
Earlier work this paper cites.
Conzales, R.C., Woods, R.E.: Multiple view geometry in computer vision. Prentice Hall (2008)
2008
Earlier work this paper cites.
Hore, A., Ziou, D.: Image quality metrics: Psnr vs. ssim. In: ICPR (2010)
2010
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: ICLR (2015)
2015
Earlier work this paper cites.
Sun, J., Cao, W., Xu, Z., Ponce, J.: Learning a convolutional neural network for non-uniform motion blur removal. In: CVPR (2015)
2015
Earlier work this paper cites.
Chakrabarti, A.: A neural approach to blind motion deblurring. In: ECCV (2016)
2016
Earlier work this paper cites.
Schonberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: CVPR (2016)
2016
Earlier work this paper cites.
Nah, S., Kim, T.H., Lee, K.M.: Deep multi-scale convolutional neural network for dynamic scene deblurring. In: CVPR (2017)
2017
Earlier work this paper cites.
Park, H., Lee, K.M.: Joint estimation of camera pose, depth, deblurring, and super-resolution from a blurred image sequence. In: ICCV (2017)
2017
Earlier work this paper cites.
Wieschollek, P., Hirsch, M., Scholkopf, B., Lensch, H.: Learning blind motion deblurring. In: ICCV (2017)
2017
Earlier work this paper cites.
Community, B.O.: Blender - a 3D modelling and rendering package. Blender Foundation, Stichting Blender Foundation, Amsterdam (2018), http://www.blender.org
2018
Earlier work this paper cites.
Tao, X., Gao, H., Shen, X., Wang, J., Jia, J.: Scale-recurrent network for deep image deblurring. In: CVPR (2018)
2018
Earlier work this paper cites.
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR (2018)
2018
Earlier work this paper cites.
Kupyn, O., Martyniuk, T., Wu, J., Wang, Z.: Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better. In: ICCV (2019)
2019
Earlier work this paper cites.
Nah, S., Baik, S., Hong, S., Moon, G., Son, S., Timofte, R., Lee, K.M.: Ntire 2019 challenge on video deblurring and super-resolution: Dataset and study. In: CVPR Workshops (2019)
2019
Earlier work this paper cites.
Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: Representing scenes as neural radiance fields for view synthesis. In: ECCV (2020)
2020
Earlier work this paper cites.
Rim, J., Lee, H., Won, J., Cho, S.: Real-world blur dataset for learning and benchmarking deblurring algorithms. In: Proceedings of the European Conference on Computer Vision (ECCV) (2020)
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
Zhang, K., Luo, W., Zhong, Y., Ma, L., Stenger, B., Liu, W., Li, H.: Deblurring by realistic blurring. In: CVPR (2020)
2020
Cited alongside, same era.
Barron, J.T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., Srinivasan, P.P.: Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In: CVPR (2021)
2021
Cited alongside, same era.
Cho, S.J., Ji, S.W., Hong, J.P., Jung, S.W., Ko, S.J.: Rethinking coarse-to-fine approach in single image deblurring. In: ICCV (2021)
2021
Cited alongside, same era.
Jeong, Y., Ahn, S., Choy, C., Anandkumar, A., Cho, M., Park, J.: Self-calibrating neural radiance fields. In: ICCV (2021)
Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., Kanazawa, A.: Plenoxels: Radiance fields without neural networks. In: CVPR (2022)
2022
Later among the works it cites.
Ma, L., Li, X., Liao, J., Zhang, Q., Wang, X., Wang, J., Sander, P.V.: Deblur-nerf: Neural radiance fields from blurry images. In: CVPR (2022)
2022
Later among the works it cites.
Müller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. TOG (2022)
2022
Later among the works it cites.
Rim, J., Kim, G., Kim, J., Lee, J., Lee, S., Cho, S.: Realistic blur synthesis for learning image deblurring. In: ECCV (2022)
2022
Later among the works it cites.
Sun, C., Sun, M., Chen, H.T.: Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. CVPR (2022)
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
Lin, C.H., Ma, W.C., Torralba, A., Lucey, S.: Barf: Bundle-adjusting neural radiance fields. In: ICCV (2021)
2021
Cited alongside, same era.
Lindell, D.B., Martel, J.N., Wetzstein, G.: Autoint: Automatic integration for fast neural volume rendering. In: CVPR (2021)
2021
Cited alongside, same era.
Reiser, C., Peng, S., Liao, Y., Geiger, A.: Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps. In: ICCV (2021)
2021
Cited alongside, same era.
Yen-Chen, L., Florence, P., Barron, J.T., Rodriguez, A., Isola, P., Lin, T.Y.: iNeRF: Inverting neural radiance fields for pose estimation. In: IROS (2021)
2021
Cited alongside, same era.
Yu, A., Li, R., Tancik, M., Li, H., Ng, R., Kanazawa, A.: Plenoctrees for real-time rendering of neural radiance fields. In: ICCV (2021)
2021
Cited alongside, same era.
Adamkiewicz, M., Chen, T., Caccavale, A., Gardner, R., Culbertson, P., Bohg, J., Schwager, M.: Vision-only robot navigation in a neural radiance world. IEEE Robotics and Automation Letters 7
2022
Cited alongside, same era.
Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In: CVPR (2022)
2022
Cited alongside, same era.
Wang, H., Ren, J., Huang, Z., Olszewski, K., Chai, M., Fu, Y., Tulyakov, S.: R2l: Distilling neural radiance field to neural light field for efficient novel view synthesis. In: ECCV (2022)
2022
Later among the works it cites.
Wu, L., Lee, J.Y., Bhattad, A., Wang, Y., Forsyth, D.: Diver: Real-time and accurate neural radiance fields with deterministic integration for volume rendering (2022)
2022
Later among the works it cites.
Xiangli, Y., Xu, L., Pan, X., Zhao, N., Rao, A., Theobalt, C., Dai, B., Lin, D.: Bungeenerf: Progressive neural radiance field for extreme multi-scale scene rendering. In: ECCV (2022)
2022
Later among the works it cites.
Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H.: Restormer: Efficient transformer for high-resolution image restoration. In: CVPR (2022)
2022
Later among the works it cites.
Bian, W., Wang, Z., Li, K., Bian, J., Prisacariu, V.A.: Nope-nerf: Optimising neural radiance field with no pose prior. In: CVPR (2023)
2023
Later among the works it cites.
Chen, Z., Funkhouser, T., Hedman, P., Tagliasacchi, A.: Mobilenerf: Exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures. In: CVPR (2023)
2023
Later among the works it cites.
Fu, Y., Liu, S., Kulkarni, A., Kautz, J., Efros, A.A., Wang, X.: Colmap-free 3d gaussian splatting (2023)
2023
Later among the works it cites.
Jiang, Y., Hedman, P., Mildenhall, B., Xu, D., Barron, J.T., Wang, Z., Xue, T.: Alignerf: High-fidelity neural radiance fields via alignment-aware training. CVPR (2023)
2023
Later among the works it cites.
Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. TOG (2023)
2023
Later among the works it cites.
Lee, D., Lee, M., Shin, C., Lee, S.: Dp-nerf: Deblurred neural radiance field with physical scene priors. In: CVPR (2023)
2023
Later among the works it cites.
Lee, D., Oh, J., Rim, J., Cho, S., Lee, K.M.: Exblurf: Efficient radiance fields for extreme motion blurred images. In: ICCV (2023)
2023
Later among the works it cites.
Wang, P., Zhao, L., Ma, R., Liu, P.: BAD-NeRF: Bundle Adjusted Deblur Neural Radiance Fields. In: CVPR (2023)
2023
Later among the works it cites.