Fetching the paper…
Reading the bibliography…
High-quality scene reconstruction and novel view synthesis based on Gaussian Splatting (3DGS) typically require steady, high-quality photographs, often impractical to capture with handheld cameras.
Triggs, B., McLauchlan, P.F., Hartley, R.I., Fitzgibbon, A.W.: Bundle adjustment - a modern synthesis. In: Proceedings of the International Workshop on Vision Algorithms: Theory and Practice. pp. 298–372. ICCV ’99, Springer-Verlag (2000)
2000
Earlier work this paper cites.
Kaipio, J., Somersalo, E.: Statistical and computational inverse problems. Springer (2004)
2004
Earlier work this paper cites.
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60
2004
Earlier work this paper cites.
Schölkopf, B., Platt, J., Hofmann, T.: Blind motion deblurring using image statistics. In: Advances in Neural Information Processing Systems 19. pp. 841–848 (2007)
2007
Earlier work this paper cites.
Liang, C.K., Chang, L.W., Chen, H.H.: Analysis and compensation of rolling shutter effect. IEEE Transactions on Image Processing 17
2008
Earlier work this paper cites.
Shan, Q., Jia, J., Agarwala, A.: High-quality motion deblurring from a single image. ACM Transactions on Graphics (TOG) 27
2008
Earlier work this paper cites.
Cai, J.F., Ji, H., Liu, C., Shen, Z.: Blind motion deblurring from a single image using sparse approximation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 104–111. IEEE (2009)
2009
Earlier work this paper cites.
Xu, L., Jia, J.: Two-phase kernel estimation for robust motion deblurring. In: European Conference on Computer Vision (ECCV). pp. 157–170 (2010)
2010
Earlier work this paper cites.
Tai, Y.W., Tan, P., Brown, M.S.: Richardson–Lucy deblurring for scenes under a projective motion path. IEEE Transactions on Pattern Analysis and Machine Intelligence 33
2011
Earlier work this paper cites.
Grundmann, M., Kwatra, V., Castro, D., Essa, I.: Calibration-free rolling shutter removal. In: IEEE International Conference on Computational Photography (ICCP). pp. 1–8 (2012)
2012
Earlier work this paper cites.
Hedborg, J., Forsén, P.E., Felsberg, M., Ringaby, E.: Rolling shutter bundle adjustment. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1434–1441 (2012)
2012
Earlier work this paper cites.
Lovegrove, S., Patron-Perez, A., Sibley, G.: Spline fusion: A continuous-time representation for visual-inertial fusion with application to rolling shutter cameras. In: Proceedings of the British Machine Vision Conference (BMVC). pp. 93.1–93.11 (2013)
2013
Earlier work this paper cites.
Su, S., Heidrich, W.: Rolling shutter motion deblurring. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1529–1537 (2015)
2015
Earlier work this paper cites.
Chakrabarti, A.: A neural approach to blind motion deblurring. In: European Conference on Computer Vision (ECCV). pp. 221–235 (2016)
2016
Earlier work this paper cites.
Rengarajan, V., Rajagopalan, A.N., Aravind, R.: From bows to arrows: Rolling shutter rectification of urban scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2773–2781 (2016)
2016
Earlier work this paper cites.
Schönberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
2016
Earlier work this paper cites.
Gong, D., Yang, J., Liu, L., Zhang, Y., Reid, I., Shen, C., van den Hengel, A., Shi, Q.: From motion blur to motion flow: A deep learning solution for removing heterogeneous motion blur. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 2319–2328 (2017)
2017
Earlier work this paper cites.
Mohan M.R., M., Rajagopalan, A., Seetharaman, G.: Going unconstrained with rolling shutter deblurring. In: IEEE International Conference on Computer Vision (ICCV). pp. 4030–4038 (2017)
2017
Cited alongside, same era.
Community, B.O.: Blender – a 3D modelling and rendering package. Blender Foundation (2018), http://www.blender.org
2018
Cited alongside, same era.
Kupyn, O., Budzan, V., Mykhailych, M., Mishkin, D., Matas, J.: DeblurGAN: Blind motion deblurring using conditional adversarial networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 8183–8192 (2018)
2018
Cited alongside, same era.
Lao, Y., Ait-Aider, O.: A robust method for strong rolling shutter effects correction using lines with automatic feature selection. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4795–4803 (2018)
2018
Cited alongside, same era.
Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3D Gaussian Splatting for real-time radiance field rendering. ACM Transactions on Graphics (TOG) 42
2023
Later among the works it cites.
Liao, B., Qu, D., Xue, Y., Zhang, H., Lao, Y.: Revisiting rolling shutter bundle adjustment: Toward accurate and fast solution. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4863–4871 (2023)
2023
Later among the works it cites.
Park, K., Henzler, P., Mildenhall, B., Barron, J.T., Martin-Brualla, R.: CamP: Camera preconditioning for neural radiance fields. ACM Transactions on Graphics (TOG) 42
2023
Later among the works it cites.
Tancik, M., Weber, E., Ng, E., Li, R., Yi, B., Kerr, J., Wang, T., Kristoffersen, A., Austin, J., Salahi, K., Ahuja, A., McAllister, D., Kanazawa, A.: Nerfstudio: A modular framework for neural radiance field development. In: ACM SIGGRAPH 2023 Conference Proceedings (2023)
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Vasu, S., Mohan M.R., M., Rajagopalan, A.: Occlusion-aware rolling shutter rectification of 3D scenes. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 636–645 (2018)
2018
Cited alongside, same era.
Schubert, D., Demmel, N., von Stumberg, L., Usenko, V., Cremers, D.: Rolling-shutter modelling for visual-inertial odometry. In: International Conference on Intelligent Robots and Systems (IROS) (2019)
2019
Cited alongside, same era.
Liu, P., Cui, Z., Larsson, V., Pollefeys, M.: Deep shutter unrolling network. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 5940–5948 (2020)
2020
Cited alongside, same era.
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: European Conference on Computer Vision (ECCV). pp. 405–421 (2020)
2020
Cited alongside, same era.
Lin, C.H., Ma, W.C., Torralba, A., Lucey, S.: BARF: Bundle-adjusting neural radiance fields. IEEE/CVF International Conference on Computer Vision (ICCV) pp. 5721–5731 (2021)
2021
Cited alongside, same era.
Zamir, S.W., Arora, A., Khan, S., Hayat, M., Khan, F.S., Yang, M.H., Shao, L.: Multi-stage progressive image restoration. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 14816–14826 (2021)
2021
Cited alongside, same era.
Fan, B., Dai, Y., Zhang, Z., Liu, Q., He, M.: Context-aware video reconstruction for rolling shutter cameras. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 17551–17561 (2022)
2022
Cited alongside, same era.
Kim, H., Song, M., Lee, D., Kim, P.: Visual-inertial odometry priors for bundle-adjusting neural radiance fields. In: International Conference on Control, Automation and Systems (ICCAS). pp. 1131–1136 (2022)
2022
Cited alongside, same era.
Wang, P., Zhao, L., Ma, R., Liu, P.: BAD-NeRF: Bundle adjusted deblur neural radiance fields. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4170–4179 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Ye, V., Kanazawa, A.: Mathematical supplement for the gsplat
2023
Later among the works it cites.
Spectacular AI mapping tools (2024), https://spectacularai.github.io/docs/sdk/tools/nerf.html , accessed: 2024-03-01
2024
Closest in time.
Keetha, N., Karhade, J., Jatavallabhula, K.M., Yang, G., Scherer, S., Ramanan, D., Luiten, J.: SplaTAM: Splat, track & map 3D Gaussians for dense RGB-D SLAM. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 21357–21366 (2024)
2024
Closest in time.
2024
Closest in time.
Li, M., Wang, P., Zhao, L., Liao, B., Liu, P.: USB-neRF: Unrolling shutter bundle adjusted neural radiance fields. In: International Conference on Learning Representations (ICLR) (2024)
2024
Closest in time.
Weber, E., Holynski, A., Jampani, V., Saxena, S., Snavely, N., Kar, A., Kanazawa, A.: NeRFiller: Completing scenes via generative 3D inpainting. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 20731–20741 (2024)
2024
Closest in time.
Yan, C., Qu, D., Xu, D., Zhao, B., Wang, Z., Wang, D., Li, X.: GS-SLAM: Dense visual SLAM with 3D Gaussian splatting. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 19595–19604 (2024)
2024
Closest in time.
2024
Closest in time.
Yu, Z., Chen, A., Huang, B., Sattler, T., Geiger, A.: Mip-Splatting: Alias-free 3D Gaussian splatting. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 19447–19456 (2024)
2024
Closest in time.