Fetching the paper…
Reading the bibliography…
Neural Radiance Fields (NeRF) presented a novel way to represent scenes, allowing for high-quality 3D reconstruction from 2D images.
G. Grisetti, C. Stachniss, and W. Burgard, “Improved techniques for grid mapping with rao-blackwellized particle filters,” IEEE transactions on Robotics , vol. 23, no. 1, pp. 34–46, 2007
2007
Earlier work this paper cites.
A. Kendall, M. Grimes, and R. Cipolla, “Posenet: A convolutional network for real-time 6-dof camera relocalization,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2938–2946
2015
Earlier work this paper cites.
R. Mur-Artal and J. D. Tardós, “Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras,” IEEE transactions on robotics , vol. 33, no. 5, pp. 1255–1262, 2017
2017
Earlier work this paper cites.
P. Hedman, J. Philip, T. Price, J.-M. Frahm, G. Drettakis, and G. Brostow, “Deep blending for free-viewpoint image-based rendering,” ACM Transactions on Graphics (ToG) , vol. 37, no. 6, pp. 1–15, 2018
2018
Earlier work this paper cites.
B. Mildenhall, P. P. Srinivasan, R. Ortiz-Cayon, N. K. Kalantari, R. Ramamoorthi, R. Ng, and A. Kar, “Local light field fusion: Practical view synthesis with prescriptive sampling guidelines,” ACM Transactions on Graphics (TOG) , vol. 38, no. 4, pp. 1–14, 2019
2019
Earlier work this paper cites.
L. Yen-Chen, “Nerf-pytorch,” https://github.com/yenchenlin/nerf-pytorch/ , 2020
2020
Earlier work this paper cites.
C. Campos, R. Elvira, J. J. G. Rodríguez, J. M. Montiel, and J. D. Tardós, “Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam,” IEEE Transactions on Robotics , vol. 37, no. 6, pp. 1874–1890, 2021
2021
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,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021
2021
Earlier work this paper cites.
L. Yen-Chen, P. Florence, J. T. Barron, A. Rodriguez, P. Isola, and T.-Y. Lin, “inerf: Inverting neural radiance fields for pose estimation,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2021, pp. 1323–1330
2021
Earlier work this paper cites.
S. Chen, Z. Wang, and V. Prisacariu, “Direct-posenet: Absolute pose regression with photometric consistency,” in 2021 International Conference on 3D Vision (3DV) . IEEE, 2021, pp. 1175–1185
2021
Earlier work this paper cites.
J. T. Barron, B. Mildenhall, M. Tancik, P. Hedman, R. Martin-Brualla, and P. P. Srinivasan, “Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 5855–5864
2021
Earlier work this paper cites.
E. Sucar, S. Liu, J. Ortiz, and A. J. Davison, “imap: Implicit mapping and positioning in real-time,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 6229–6238
2021
Earlier work this paper cites.
A. Pumarola, E. Corona, G. Pons-Moll, and F. Moreno-Noguer, “D-nerf: Neural radiance fields for dynamic scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 10 318–10 327
2021
Earlier work this paper cites.
S. Zhi, T. Laidlow, S. Leutenegger, and A. J. Davison, “In-place scene labelling and understanding with implicit scene representation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 838–15 847
2021
Cited alongside, same era.
A. Moreau, N. Piasco, D. Tsishkou, B. Stanciulescu, and A. de La Fortelle, “Lens: Localization enhanced by nerf synthesis,” in Conference on Robot Learning . PMLR, 2022, pp. 1347–1356
2022
Cited alongside, same era.
S. Chen, X. Li, Z. Wang, and V. A. Prisacariu, “Dfnet: Enhance absolute pose regression with direct feature matching,” in European Conference on Computer Vision . Springer, 2022, pp. 1–17
2022
Cited alongside, same era.
J. T. Barron, B. Mildenhall, D. Verbin, P. P. Srinivasan, and P. Hedman, “Mip-nerf 360: Unbounded anti-aliased neural radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 5470–5479
2022
Cited alongside, same era.
Y. Lin, T. Müller, J. Tremblay, B. Wen, S. Tyree, A. Evans, P. A. Vela, and S. Birchfield, “Parallel inversion of neural radiance fields for robust pose estimation,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 9377–9384
2023
Later among the works it cites.
D. Maggio, M. Abate, J. Shi, C. Mario, and L. Carlone, “Loc-nerf: Monte carlo localization using neural radiance fields,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 4018–4025
2023
Later among the works it cites.
2023
Later among the works it cites.
Z. Mi and D. Xu, “Switch-nerf: Learning scene decomposition with mixture of experts for large-scale neural radiance fields,” in International Conference on Learning Representations (ICLR) , 2023. [Online]. Available: https://openreview.net/forum?id=PQ2zoIZqvm
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
T. Müller, A. Evans, C. Schied, and A. Keller, “Instant neural graphics primitives with a multiresolution hash encoding,” ACM Transactions on Graphics (ToG) , vol. 41, no. 4, pp. 1–15, 2022
2022
Cited alongside, same era.
S. Fridovich-Keil, A. Yu, M. Tancik, Q. Chen, B. Recht, and A. Kanazawa, “Plenoxels: Radiance fields without neural networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 5501–5510
2022
Cited alongside, same era.
A. Chen, Z. Xu, A. Geiger, J. Yu, and H. Su, “Tensorf: Tensorial radiance fields,” in European Conference on Computer Vision . Springer, 2022, pp. 333–350
2022
Cited alongside, same era.
C. Sun, M. Sun, and H.-T. Chen, “Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction,” CVPR , 2022
2022
Cited alongside, same era.
H. Turki, D. Ramanan, and M. Satyanarayanan, “Mega-nerf: Scalable construction of large-scale nerfs for virtual fly-throughs,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 12 922–12 931
2022
Cited alongside, same era.
M. Tancik, V. Casser, X. Yan, S. Pradhan, B. Mildenhall, P. P. Srinivasan, J. T. Barron, and H. Kretzschmar, “Block-nerf: Scalable large scene neural view synthesis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 8248–8258
2022
Cited alongside, same era.
K. Deng, A. Liu, J.-Y. Zhu, and D. Ramanan, “Depth-supervised nerf: Fewer views and faster training for free,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 12 882–12 891
2022
Cited alongside, same era.
Z. Zhu, S. Peng, V. Larsson, W. Xu, H. Bao, Z. Cui, M. R. Oswald, and M. Pollefeys, “Nice-slam: Neural implicit scalable encoding for slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 12 786–12 796
2022
Cited alongside, same era.
2023
Later among the works it cites.
W. Bian, Z. Wang, K. Li, J.-W. Bian, and V. A. Prisacariu, “Nope-nerf: Optimising neural radiance field with no pose prior,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 4160–4169
2023
Later among the works it cites.
E. Sandström, Y. Li, L. Van Gool, and M. R. Oswald, “Point-slam: Dense neural point cloud-based slam,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 18 433–18 444
2023
Later among the works it cites.
A. Rosinol, J. J. Leonard, and L. Carlone, “Nerf-slam: Real-time dense monocular slam with neural radiance fields,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 3437–3444
2023
Later among the works it cites.
A. Mirzaei, T. Aumentado-Armstrong, K. G. Derpanis, J. Kelly, M. A. Brubaker, I. Gilitschenski, and A. Levinshtein, “Spin-nerf: Multiview segmentation and perceptual inpainting with neural radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 20 669–20 679
2023
Later among the works it cites.
M. Kong, S. Lee, and E. Kim, “Roomnerf: Representing empty room as neural radiance fields for view synthesis,” in British Machine Vision Conference , 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:267000673
2023
Later among the works it cites.
S. Fridovich-Keil, G. Meanti, F. R. Warburg, B. Recht, and A. Kanazawa, “K-planes: Explicit radiance fields in space, time, and appearance,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 12 479–12 488
2023
Later among the works it cites.
Z. Li, Q. Wang, F. Cole, R. Tucker, and N. Snavely, “Dynibar: Neural dynamic image-based rendering,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 4273–4284
2023
Later among the works it cites.
J. Cen, Z. Zhou, J. Fang, W. Shen, L. Xie, D. Jiang, X. Zhang, Q. Tian et al. , “Segment anything in 3d with nerfs,” Advances in Neural Information Processing Systems , vol. 36, pp. 25 971–25 990, 2023
2023
Later among the works it cites.