Built on

C. Tomasi and T. Kanade, “Shape and motion from image streams: a factorization method.” Proceedings of the National Academy of Sciences , vol. 90, no. 21, pp. 9795–9802, 1993
1993
Earlier work this paper cites.
R. Hartley and A. Zisserman, “Multiple view geometry in computer vision second edition,” Cambridge University Press , 2000
2000
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,” CoRR , vol. abs/2003.08934, 2020. [Online]. Available: https://arxiv.org/abs/2003.08934
Original
2003
Earlier work this paper cites.
G. Lecot and B. Levy, “Ardeco: Automatic region detection and conversion,” in 17th Eurographics Symposium on Rendering-EGSR’06 , 2006, pp. 349–360
2006
Earlier work this paper cites.
K. Zhang, G. Riegler, N. Snavely, and V. Koltun, “NeRF++: Analyzing and Improving Neural Radiance Fields,” 2020. [Online]. Available: https://arxiv.org/abs/2010.07492
Original
2010
Earlier work this paper cites.
D. Rebain, W. Jiang, S. Yazdani, K. Li, K. M. Yi, and A. Tagliasacchi, “Derf: Decomposed radiance fields,” 2020. [Online]. Available: https://arxiv.org/abs/2011.12490
Original
2011
Earlier work this paper cites.
V. Nivoliers and B. Lévy, “Approximating functions on a mesh with restricted voronoi diagrams,” in Computer Graphics Forum , vol. 32, no. 5. Wiley Online Library, 2013, pp. 83–92
2013
Earlier work this paper cites.
Y. Xiao, Z. Chen, J. Cao, Y. J. Zhang, and C. Wang, “Optimal power diagrams via function approximation,” Computer-Aided Design , vol. 102, pp. 52–60, 2018
2018
Earlier work this paper cites.
V. Sitzmann, M. Zollhöfer, and G. Wetzstein, “Scene representation networks: Continuous 3d-structure-aware neural scene representations,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Earlier work this paper cites.
S. Lombardi, T. Simon, J. Saragih, G. Schwartz, A. Lehrmann, and Y. Sheikh, “Neural volumes: Learning dynamic renderable volumes from images,” arXiv preprint arXiv:1906.07751 , 2019
Original
2019
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.
K. Zhang, G. Riegler, N. Snavely, and V. Koltun, “Nerf++: Analyzing and improving neural radiance fields,” arXiv preprint arXiv:2010.07492 , 2020
Original
2020
Earlier work this paper cites.
L. Liu, J. Gu, K. Z. Lin, T.-S. Chua, and C. Theobalt, “Neural sparse voxel fields,” NeurIPS , 2020
2020
Earlier work this paper cites.

Similar

D. Rebain, W. Jiang, S. Yazdani, K. Li, K. M. Yi, and A. Tagliasacchi, “DeRF: Decomposed Radiance Fields,” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 14 148–14 156, 2020
2020
Cited alongside, same era.
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,” CoRR , vol. abs/2103.13415, 2021. [Online]. Available: https://arxiv.org/abs/2103.13415
Original
2021
Cited alongside, same era.
T. Neff, P. Stadlbauer, M. Parger, A. Kurz, J. H. Mueller, C. R. A. Chaitanya, A. Kaplanyan, and M. Steinberger, “DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks,” Computer Graphics Forum , vol. 40, no. 4, pp. 45–59, 2021. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14340
2021
Cited alongside, same era.
A. Yu, V. Ye, M. Tancik, and A. Kanazawa, “pixelNeRF: Neural Radiance Fields from One or Few Images,” in CVPR , 2021

Then

A. Kurz, T. Neff, Z. Lv, M. Zollhöfer, and M. Steinberger, “AdaNeRF: Adaptive Sampling for Real-Time Rendering of Neural Radiance Fields,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XVII . Springer, 2022, pp. 254–270
2022
Later among the works it cites.
Q. Xu, Z. Xu, J. Philip, S. Bi, Z. Shu, K. Sunkavalli, and U. Neumann, “Point-NeRF: Point-based Neural Radiance Fields,” arXiv preprint arXiv:2201.08845 , 2022
Original
2022
Later among the works it cites.
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
Later among the works it cites.
B. Roessle, J. T. Barron, B. Mildenhall, P. P. Srinivasan, and M. Nießner, “Dense depth priors for neural radiance fields from sparse input views,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 12 892–12 901

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2023

Adaptive Voronoi NeRFs

Elsner, Tim, Czech, Victor, Berger, Julia et al.

Understand

Neural Radiance Fields (NeRFs) learn to represent a 3D scene from just a set of registered images.

Increasing sizes of a scene demands more complex functions, typically represented by neural networks, to capture all details.
Training and inference then involves querying the neural network millions of times per image, which becomes impractically slow.
Since such complex functions can be replaced by multiple simpler functions to improve speed, we show that a hierarchy of Voronoi diagrams is a suitable choice to partition the scene.

Built on

C. Tomasi and T. Kanade, “Shape and motion from image streams: a factorization method.” Proceedings of the National Academy of Sciences , vol. 90, no. 21, pp. 9795–9802, 1993
1993
Earlier work this paper cites.
R. Hartley and A. Zisserman, “Multiple view geometry in computer vision second edition,” Cambridge University Press , 2000
2000
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,” CoRR , vol. abs/2003.08934, 2020. [Online]. Available: https://arxiv.org/abs/2003.08934
Original
2003
Earlier work this paper cites.
G. Lecot and B. Levy, “Ardeco: Automatic region detection and conversion,” in 17th Eurographics Symposium on Rendering-EGSR’06 , 2006, pp. 349–360
2006
Earlier work this paper cites.
K. Zhang, G. Riegler, N. Snavely, and V. Koltun, “NeRF++: Analyzing and Improving Neural Radiance Fields,” 2020. [Online]. Available: https://arxiv.org/abs/2010.07492
Original
2010
Earlier work this paper cites.
D. Rebain, W. Jiang, S. Yazdani, K. Li, K. M. Yi, and A. Tagliasacchi, “Derf: Decomposed radiance fields,” 2020. [Online]. Available: https://arxiv.org/abs/2011.12490
Original
2011
Earlier work this paper cites.
V. Nivoliers and B. Lévy, “Approximating functions on a mesh with restricted voronoi diagrams,” in Computer Graphics Forum , vol. 32, no. 5. Wiley Online Library, 2013, pp. 83–92
2013
Earlier work this paper cites.
Y. Xiao, Z. Chen, J. Cao, Y. J. Zhang, and C. Wang, “Optimal power diagrams via function approximation,” Computer-Aided Design , vol. 102, pp. 52–60, 2018
2018
Earlier work this paper cites.
V. Sitzmann, M. Zollhöfer, and G. Wetzstein, “Scene representation networks: Continuous 3d-structure-aware neural scene representations,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Earlier work this paper cites.
S. Lombardi, T. Simon, J. Saragih, G. Schwartz, A. Lehrmann, and Y. Sheikh, “Neural volumes: Learning dynamic renderable volumes from images,” arXiv preprint arXiv:1906.07751 , 2019
Original
2019
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.
K. Zhang, G. Riegler, N. Snavely, and V. Koltun, “Nerf++: Analyzing and improving neural radiance fields,” arXiv preprint arXiv:2010.07492 , 2020
Original
2020
Earlier work this paper cites.
L. Liu, J. Gu, K. Z. Lin, T.-S. Chua, and C. Theobalt, “Neural sparse voxel fields,” NeurIPS , 2020
2020
Earlier work this paper cites.

Similar

D. Rebain, W. Jiang, S. Yazdani, K. Li, K. M. Yi, and A. Tagliasacchi, “DeRF: Decomposed Radiance Fields,” 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 14 148–14 156, 2020
2020
Cited alongside, same era.
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,” CoRR , vol. abs/2103.13415, 2021. [Online]. Available: https://arxiv.org/abs/2103.13415
Original
2021
Cited alongside, same era.
T. Neff, P. Stadlbauer, M. Parger, A. Kurz, J. H. Mueller, C. R. A. Chaitanya, A. Kaplanyan, and M. Steinberger, “DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks,” Computer Graphics Forum , vol. 40, no. 4, pp. 45–59, 2021. [Online]. Available: https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14340
2021
Cited alongside, same era.
A. Yu, V. Ye, M. Tancik, and A. Kanazawa, “pixelNeRF: Neural Radiance Fields from One or Few Images,” in CVPR , 2021

Then

A. Kurz, T. Neff, Z. Lv, M. Zollhöfer, and M. Steinberger, “AdaNeRF: Adaptive Sampling for Real-Time Rendering of Neural Radiance Fields,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XVII . Springer, 2022, pp. 254–270
2022
Later among the works it cites.
Q. Xu, Z. Xu, J. Philip, S. Bi, Z. Shu, K. Sunkavalli, and U. Neumann, “Point-NeRF: Point-based Neural Radiance Fields,” arXiv preprint arXiv:2201.08845 , 2022
Original
2022
Later among the works it cites.
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
Later among the works it cites.
B. Roessle, J. T. Barron, B. Mildenhall, P. P. Srinivasan, and M. Nießner, “Dense depth priors for neural radiance fields from sparse input views,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 12 892–12 901

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

2021

Cited alongside, same era.

R. Martin-Brualla, N. Radwan, M. S. Sajjadi, J. T. Barron, A. Dosovitskiy, and D. Duckworth, “Nerf in the wild: Neural radiance fields for unconstrained photo collections,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 7210–7219

2021

Cited alongside, same era.

M. Niemeyer and A. Geiger, “Giraffe: Representing scenes as compositional generative neural feature fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 11 453–11 464

2021

Cited alongside, same era.

K. Park, U. Sinha, J. T. Barron, S. Bouaziz, D. B. Goldman, S. M. Seitz, and R. Martin-Brualla, “Nerfies: Deformable neural radiance fields,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2021, pp. 5865–5874

2021

Cited alongside, same era.

P. Hedman, P. P. Srinivasan, B. Mildenhall, J. T. Barron, and P. Debevec, “Baking Neural Radiance Fields for Real-Time View Synthesis,” ICCV , 2021

2021

Cited alongside, same era.

A. Yu, R. Li, M. Tancik, H. Li, R. Ng, and A. Kanazawa, “PlenOctrees for Real-time Rendering of Neural Radiance Fields,” in ICCV , 2021

2021

Cited alongside, same era.

C. Reiser, S. Peng, Y. Liao, and A. Geiger, “KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs,” in International Conference on Computer Vision (ICCV) , 2021

2021

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,” CVPR , 2022

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,” 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 8238–8248, 2022

2022

Cited alongside, same era.

2022