The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
K-Planes: Explicit Radiance Fields in Space, Time, and Appearance
Open accessAbstract
We introduce k-planes, a white-box model for radiance fields in arbitrary dimensions. Our model uses planes to represent a d-dimensional scene, providing a seamless way to go from static (d = 3) to dynamic (d= 4) scenes. This planar factorization makes adding dimension-specific priors easy, e.g. temporal smoothness and multi-resolution spatial structure, and induces a natural decomposition of static and dynamic components of a scene. We use a linear feature decoder with a learned color basis that yields similar performance as a nonlinear black-box MLP decoder. Across a range of synthetic and real, static and dynamic, fixed and varying appearance scenes, k-planes yields competitive and often state-of-the-art recon- struction fidelity with low memory usage, achieving 1000x compression over a full 4D grid, and fast optimization with a pure PyTorch implementation. For video results and code, please see sarafridov.github.io/K-Planes.
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Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-58452-8_24 · Cited by 5,351
Instant neural graphics primitives with a multiresolution hash encoding
Müller, T., Evans, A., Schied, C., & Keller, A. (2022) · ACM Transactions on Graphics · doi:10.1145/3528223.3530127 · Cited by 4,114
Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields
Barron, J. T., Mildenhall, B., Verbin, D., Srinivasan, P. P., & Hedman, P. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00539 · Cited by 1,935
Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields
Barron, J. T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., & Srinivasan, P. P. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00580 · Cited by 1,921
Plenoxels: Radiance Fields without Neural Networks
Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., & Kanazawa, A. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.00542 · Cited by 1,388
Self-citationNeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections
Martin-Brualla, R., Radwan, N., Sajjadi, M. S. M., Barron, J. T., Dosovitskiy, A., & Duckworth, D. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00713 · Cited by 1,374
D-NeRF: Neural Radiance Fields for Dynamic Scenes
Pumarola, A., Corona, E., Pons-Moll, G., & Moreno-Noguer, F. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01018 · Cited by 1,355
TensoRF: Tensorial Radiance Fields
Chen, A., Xu, Z., Geiger, A., Yu, J., & Su, H. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-19824-3_20 · Cited by 1,184
Local light field fusion: practical view synthesis with prescriptive sampling guidelines
Mildenhall, B., Srinivasan, P. P., Ortiz-Cayon, R., Kalantari, N. K., Ramamoorthi, R., Ng, R., & Kar, A. (2019) · ACM Transactions on Graphics · doi:10.1145/3306346.3322980 · Cited by 1,175
Cited by
Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis
Luiten, J., Kopanas, G., Leibe, B., & Ramanan, D. (2024) · 2024 International Conference on 3D Vision (3DV) · doi:10.1109/3dv62453.2024.00044 · Cited by 469
Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction
Yang, Z., Gao, X., Zhou, W., Jiao, S., Zhang, Y., & Jin, X. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01922 · Cited by 452
HexPlane: A Fast Representation for Dynamic Scenes
Cao, A., & Johnson, J. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52729.2023.00021 · Cited by 435
Compact 3D Gaussian Representation for Radiance Field
Lee, J. C., Rho, D., Sun, X., Ko, J. H., & Park, E. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.02052 · Cited by 269
DriveDreamer4D: World Models Are Effective Data Machines for 4D Driving Scene Representation
Zhao, G., Ni, C., Wang, X., Zhu, Z., Zhang, X., Wang, Y., Huang, G., Chen, X., Wang, B., Zhang, Y., et al. (2024) · arXiv · doi:10.48550/arxiv.2410.13571 · Cited by 41
Metadata
- Authors
- Fridovich-Keil, S., Meanti, G., Warburg, F. R., Recht, B., & Kanazawa, A.
- Venue
- 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2023-06
- Pages
- 12479-12488
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4386075614
- Cited by
- 560
- References
- 36
- FWCI
- 122.59
- Type
- conference paper
- Language
- en
- Open access
- green
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- Fridovich-Keil, S., Meanti, G., Warburg, F. R., Recht, B., & Kanazawa, A. (2023). K-Planes: Explicit Radiance Fields in Space, Time, and Appearance. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52729.2023.01201
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