SG-NeRF: Sparse-Input Generalized Neural Radiance Fields for Novel View Synthesis
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11390-024-4157-6.pdf
Reference44 articles.
1. Mildenhall B, Srinivasan P P, Tancik M, Barron J T, Ramamoorthi R, Ng R. NeRF: Representing scenes as neural radiance fields for view synthesis. Communications of the ACM, 2022, 65(1): 99–106. DOI: https://doi.org/10.1145/3503250.
2. Yu A, Ye V, Tancik M, Kanazawa A. pixelNeRF: Neural radiance fields from one or few images. In Proc. the 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2021, pp.4576–4585. DOI: https://doi.org/10.1109/CVPR46437.2021.00455.
3. Trevithick A, Yang B. GRF: Learning a general radiance field for 3D representation and rendering. In Proc. the 2021 IEEE/CVF International Conference on Computer Vision, Oct. 2021, pp.15162–15172. DOI: https://doi.org/10.1109/ICCV48922.2021.01490.
4. Li J X, Feng Z J, She Q, Ding H H, Wang C H, Lee G H. MINE: Towards continuous depth MPI with NeRF for novel view synthesis. In Proc. the 2021 IEEE/CVF International Conference on Computer Vision, Oct. 2021, pp.12558–12568. DOI: https://doi.org/10.1109/ICCV48922.2021.01235.
5. Deng K D, Liu A, Zhu J Y, Ramanan D. Depth-supervised NeRF: Fewer views and faster training for free. In Proc. the 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun. 2022, pp.12872–12881. DOI: https://doi.org/10.1109/CVPR52688.2022.01254.
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