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In this paper, we present a Scale-adaptive method for Anti-aliasing Gaussian Splatting (SA-GS).
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Barron, J.T., Mildenhall, B., Tancik, M., Hedman, P., Martin-Brualla, R., Srinivasan, P.P.: Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 5855–5864 (2021)
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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 65
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Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5470–5479 (2022)
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Fridovich-Keil, S., Yu, A., Tancik, M., Chen, Q., Recht, B., Kanazawa, A.: Plenoxels: Radiance fields without neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5501–5510 (2022)
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Müller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Transactions on Graphics (ToG) 41
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Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering (2023)
2023
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2023
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Wu, Z., Liu, T., Luo, L., Zhong, Z., Chen, J., Xiao, H., Hou, C., Lou, H., Chen, Y., Yang, R., et al.: Mars: An instance-aware, modular and realistic simulator for autonomous driving. In: CAAI International Conference on Artificial Intelligence. pp. 3–15. Springer (2023)
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Xu, Q., Xu, Z., Philip, J., Bi, S., Shu, Z., Sunkavalli, K., Neumann, U.: Point-nerf: Point-based neural radiance fields. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) pp. 5428–5438 (2022), https://api.semanticscholar.org/CorpusID:246210101
2022
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Hu, W., Wang, Y., Ma, L., Yang, B., Gao, L., Liu, X., Ma, Y.: Tri-miprf: Tri-mip representation for efficient anti-aliasing neural radiance fields. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 19774–19783 (2023)
2023
Cited alongside, same era.
Hu, W., Wang, Y., Ma, L., Yang, B., Gao, L., Liu, X., Ma, Y.: Tri-miprf: Tri-mip representation for efficient anti-aliasing neural radiance fields (2023)
2023
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2023
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Yuan, S., Zhao, H.: Slimmerf: Slimmable radiance fields. arXiv preprint arXiv:2312.10034 (2023)
2023
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2023
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2024
Closest in time.
Zhou, Q., Li, W., Jiang, L., Wang, G., Zhou, G., Zhang, S., Zhao, H.: Pad: A dataset and benchmark for pose-agnostic anomaly detection. Advances in Neural Information Processing Systems 36
2024
Closest in time.