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While neural rendering has demonstrated impressive capabilities in 3D scene reconstruction and novel view synthesis, it heavily relies on high-quality sharp images and accurate camera poses.
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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) (2020), https://www.matthewtancik.com/nerf
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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: International Conference on Computer Vision (ICCV) (2021), https://jonbarron.info/mipnerf/
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Garbin, S.J., Kowalski, M., Johnson, M., Shotton, J., Valentin, J.: FastNeRF: High-Fidelity Neural Rendering at 200FPS. In: International Conference on Computer Vision (ICCV) (2021), https://microsoft.github.io/FastNeRF/
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Müller, T., Evans, A., Schied, C., Keller, A.: Instant Neural Graphics Primitives with a Multiresolution Hash Encoding. ACM Transactions on Graphics (TOG) 41
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Sara Fridovich-Keil and Alex Yu, Tancik, M., Chen, Q., Recht, B., Kanazawa, A.: Plenoxels: Radiance Fields without Neural Networks. In: Computer Vision and Pattern Recognition (CVPR) (2022), https://alexyu.net/plenoxels/
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2021
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Jeong, Y., Ahn, S., Choy, C., Anandkumar, A., Cho, M., Park, J.: Self-Calibrating Neural Radiance Fields. In: International Conference on Computer Vision (ICCV) (2021), https://postech-cvlab.github.io/SCNeRF/
2021
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Lassner, C., Zollhofer, M.: Pulsar: Efficient sphere-based neural rendering. In: Computer Vision and Pattern Recognition (CVPR) (2021), https://github.com/facebookresearch/pytorch3d/
2021
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Lin, Chen-Hsuan and Ma, Wei-Chiu and Torralba, Antonio and Lucey, Simon: BARF: Bundle-Adjusting Neural Radiance Fields. In: International Conference on Computer Vision (ICCV) (2021), https://chenhsuanlin.bitbucket.io/bundle-adjusting-NeRF
2021
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Liu, P., Zuo, X., Larsson, V., Pollefeys, M.: MBA-VO: Motion Blur Aware Visual Odometry. In: International Conference on Computer Vision (ICCV) (2021), https://github.com/ethliup/MBA-VO
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Piala, M., Clark, R.: TermiNeRF: Ray Termination Prediction for Efficient Neural Rendering. In: International Conference on 3D Vision (3DV) (2021), https://projects.mackopes.com/terminerf/
2021
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2021
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Wizadwongsa, S., Phongthawee, P., Yenphraphai, J., Suwajanakorn, S.: NeX: Real-time View Synthesis with Neural Basis Expansion. In: Computer Vision and Pattern Recognition (CVPR) (2021), https://nex-mpi.github.io/
2021
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Yu, A., Li, R., Tancik, M., Li, H., Ng, R., Kanazawa, A.: PlenOctrees for Real-time Rendering of Neural Radiance Fields. In: International Conference on Computer Vision (ICCV) (2021), https://alexyu.net/plenoctrees/
2021
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Sun, C., Sun, M., Chen, H.: Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction. In: Computer Vision and Pattern Recognition (CVPR) (2022), https://sunset1995.github.io/dvgo/
2022
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Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields. In: International Conference on Computer Vision (ICCV) (2023), https://jonbarron.info/zipnerf/
2023
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Cao, A., Johnson, J.: HexPlane: A Fast Representation for Dynamic Scenes. In: Computer Vision and Pattern Recognition (CVPR) (2023), https://caoang327.github.io/HexPlane/
2023
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Fridovich-Keil, S., Meanti, G., Warburg, F.R., Recht, B., Kanazawa, A.: K-Planes: Explicit Radiance Fields in Space, Time, and Appearance. In: Computer Vision and Pattern Recognition (CVPR) (2023), https://sarafridov.github.io/K-Planes/
2023
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2023
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Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3D Gaussian Splatting for Real-Time Radiance Field Rendering. ACM Transactions on Graphics (TOG) 42
2023
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Lee, D., Lee, M., Shin, C., Lee, S.: DP-NeRF: Deblurred Neural Radiance Field With Physical Scene Priors. In: Computer Vision and Pattern Recognition (CVPR) (2023), https://dogyoonlee.github.io/dpnerf/
2023
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Park, K., Henzler, P., Mildenhall, B., Barron, J.T., Martin-Brualla, R.: CamP: Camera Preconditioning for Neural Radiance Fields. ACM Transactions on Graphics (TOG) (2023), https://camp-nerf.github.io/
2023
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Wang, C., Gao, D., Xu, K., Geng, J., Hu, Y., Qiu, Y., Li, B., Yang, F., Moon, B., Pandey, A., Aryan, Xu, J., Wu, T., He, H., Huang, D., Ren, Z., Zhao, S., Fu, T., Reddy, P., Lin, X., Wang, W., Shi, J., Talak, R., Cao, K., Du, Y., Wang, H., Yu, H., Wang, S., Chen, S., Kashyap, A., Bandaru, R., Dantu, K., Wu, J., Xie, L., Carlone, L., Hutter, M., Scherer, S.: PyPose: A library for robot learning with physics-based optimization. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023), https://github.com/pypose/pypose
2023
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Wang, P., Zhao, L., Ma, R., Liu, P.: BAD-NeRF: Bundle Adjusted Deblur Neural Radiance Fields. In: Computer Vision and Pattern Recognition (CVPR) (2023), https://wangpeng000.github.io/BAD-NeRF/
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
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), https://moyangli00.github.io/usb_nerf/
2024
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