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Recently neural radiance fields (NeRF) have been widely exploited as 3D representations for dense simultaneous localization and mapping (SLAM).
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Kerl, C., Sturm, J., Cremers, D.: Dense visual slam for rgb-d cameras. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 2100–2106. IEEE (2013)
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Tateno, K., Tombari, F., Laina, I., Navab, N.: CNN-SLAM: real-time dense monocular SLAM with learned depth prediction. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017. pp. 6565–6574. IEEE Computer Society (2017). https://doi.org/10.1109/CVPR.2017.695, https://doi.org/10.1109/CVPR.2017.695
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Vespa, E., Nikolov, N., Grimm, M., Nardi, L., Kelly, P.H.J., Leutenegger, S.: Efficient octree-based volumetric SLAM supporting signed-distance and occupancy mapping. IEEE Robotics Autom. Lett. 3
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Schops, T., Sattler, T., Pollefeys, M.: Bad slam: Bundle adjusted direct rgb-d slam. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
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Straub, J., Whelan, T., Ma, L., Chen, Y., Wijmans, E., Green, S., Engel, J.J., Mur-Artal, R., Ren, C., Verma, S., Clarkson, A., Yan, M., Budge, B., Yan, Y., Pan, X., Yon, J., Zou, Y., Leon, K., Carter, N., Briales, J., Gillingham, T., Mueggler, E., Pesqueira, L., Savva, M., Batra, D., Strasdat, H.M., Nardi, R.D., Goesele, M., Lovegrove, S., Newcombe, R.: The replica dataset: A digital replica of indoor spaces (2019)
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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: The European Conference on Computer Vision (ECCV) (2020)
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Yang, N., von Stumberg, L., Wang, R., Cremers, D.: D3VO: deep depth, deep pose and deep uncertainty for monocular visual odometry. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020. pp. 1278–1289. Computer Vision Foundation / IEEE (2020). https://doi.org/10.1109/CVPR42600.2020.00136, https://openaccess.thecvf.com/content_CVPR_2020/html/Yang_D3VO_Deep_Depth_Deep_Pose_and_Deep_Uncertainty_for_Monocular_CVPR_2020_paper.html
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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 (2021)
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Koestler, L., Yang, N., Zeller, N., Cremers, D.: Tandem: Tracking and dense mapping in real-time using deep multi-view stereo. In: Conference on Robot Learning (CoRL) (2021)
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Lin, C.H., Ma, W.C., Torralba, A., Lucey, S.: Barf: Bundle-adjusting neural radiance fields. In: IEEE International Conference on Computer Vision (ICCV) (2021)
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Geng, C., Peng, S., Xu, Z., Bao, H., Zhou, X.: Learning neural volumetric representations of dynamic humans in minutes. In: CVPR (2023)
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2023
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2021
Cited alongside, same era.
Sucar, E., Liu, S., Ortiz, J., Davison, A.J.: imap: Implicit mapping and positioning in real-time. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 6229–6238 (2021)
2021
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Teed, Z., Deng, J.: DROID-SLAM: deep visual SLAM for monocular, stereo, and RGB-D cameras. In: Ranzato, M., Beygelzimer, A., Dauphin, Y.N., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual. pp. 16558–16569 (2021), https://proceedings.neurips.cc/paper/2021/hash/89fcd07f20b6785b92134bd6c1d0fa42-Abstract.html
2021
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Yu, A., Ye, V., Tancik, M., Kanazawa, A.: pixelNeRF: Neural radiance fields from one or few images. In: CVPR (2021)
2021
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Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-nerf 360: Unbounded anti-aliased neural radiance fields. CVPR (2022)
2022
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Fang, J., Yi, T., Wang, X., Xie, L., Zhang, X., Liu, W., Nießner, M., Tian, Q.: Fast dynamic radiance fields with time-aware neural voxels. In: SIGGRAPH Asia 2022 Conference Papers (2022)
2022
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Fridovich-Keil and Yu, Tancik, M., Chen, Q., Recht, B., Kanazawa, A.: Plenoxels: Radiance fields without neural networks. In: CVPR (2022)
2022
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Müller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Trans. Graph. 41
2022
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2022
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2023
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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 42
2023
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Pang, H., Zhu, H., Kortylewski, A., Theobalt, C., Habermann, M.: Ash: Animatable gaussian splats for efficient and photoreal human rendering (2023)
2023
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Rosinol, A., Leonard, J.J., Carlone, L.: Nerf-slam: Real-time dense monocular SLAM with neural radiance fields. In: IROS. pp. 3437–3444 (2023). https://doi.org/10.1109/IROS55552.2023.10341922, https://doi.org/10.1109/IROS55552.2023.10341922
2023
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Sandström, E., Li, Y., Van Gool, L., Oswald, M.R.: Point-slam: Dense neural point cloud-based slam. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 18433–18444 (2023)
2023
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2023
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Shao, R., Zheng, Z., Tu, H., Liu, B., Zhang, H., Liu, Y.: Tensor4d: Efficient neural 4d decomposition for high-fidelity dynamic reconstruction and rendering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2023)
2023
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Wang, H., Wang, J., Agapito, L.: Co-slam: Joint coordinate and sparse parametric encodings for neural real-time slam. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13293–13302 (2023)
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Zielonka, W., Bagautdinov, T., Saito, S., Zollhöfer, M., Thies, J., Romero, J.: Drivable 3d gaussian avatars (2023)
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Tretschk, E., Golyanik, V., Zollhöfer, M., Bozic, A., Lassner, C., Theobalt, C.: Scenerflow: Time-consistent reconstruction of general dynamic scenes. In: International Conference on 3D Vision (3DV) (2024)
2024
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Yan, C., Qu, D., Wang, D., Xu, D., Wang, Z., Zhao, B., Li, X.: Gs-slam: Dense visual slam with 3d gaussian splatting (2024)
2024
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