The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Plenoxels: Radiance Fields without Neural Networks
Abstract
We introduce Plenoxels (plenoptic voxels), a systemfor photorealistic view synthesis. Plenoxels represent a scene as a sparse 3D grid with spherical harmonics. This representation can be optimized from calibrated images via gradient methods and regularization without any neural components. On standard, benchmark tasks, Plenoxels are optimized two orders of magnitude faster than Neural Radiance Fields with no loss in visual quality. For video and code, please see https://alexyu.net/plenoxels.
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Citations
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
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623
NeRF: representing scenes as neural radiance fields for view synthesis
Mildenhall, B., Srinivasan, P. P., Tancik, M., Barron, J. T., Ramamoorthi, R., & Ng, R. (2022) · Communications of the ACM · doi:10.1145/3503250 · Cited by 6,852
Self-citationStereo Magnification: Learning View Synthesis using Multiplane Images
Zhou, T., Tucker, R., Flynn, J., Fyffe, G., & Snavely, N. (2018) · arXiv · doi:10.48550/arxiv.1805.09817 · Cited by 150 · cited 2×
“Real 360◦ Scenes We extend our method to real, unbounded 360◦ scenes by surrounding our sparse voxel grid with an multi-sphere image (MSI, based on multi-plane images introduced by [62]) background model, in which each background sphere is also a simple voxel grid with trilinear interpolation (both within each sphere…”
Method
Cited by
3D Gaussian Splatting for Real-Time Radiance Field Rendering
Kerbl, B., Kopanas, G., Leimkuehler, T., & Drettakis, G. (2023) · ACM Transactions on Graphics · doi:10.1145/3592433 · Cited by 6,025
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
Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly- Throughs
Turki, H., Ramanan, D., & Satyanarayanan, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01258 · Cited by 423
PixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction
Charatan, D., Li, S. L., Tagliasacchi, A., & Sitzmann, V. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52733.2024.01840 · Cited by 420
ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes
Yeshwanth, C., Liu, Y.-C., Nießner, M., & Dai, A. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00008 · Cited by 316
Metadata
- Authors
- Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., & Kanazawa, A.
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 5491-5500
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4312325284
- Cited by
- 1,388
- References
- 4
- FWCI
- 348.42
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., & Kanazawa, A. (2022). Plenoxels: Radiance Fields without Neural Networks. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr52688.2022.00542
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