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The development of Neural Radiance Fields (NeRFs) has provided a potent representation for encapsulating the geometric and appearance characteristics of 3D scenes.
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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,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 5855–5864
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A. Yu, V. Ye, M. Tancik, and A. Kanazawa, “pixelnerf: Neural radiance fields from one or few images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 4578–4587
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C. Zhou, C. C. Loy, and B. Dai, “Extract free dense labels from clip,” in Proceedings of the European Conference on Computer Vision . Springer, 2022, pp. 696–712
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J. T. Barron, B. Mildenhall, D. Verbin, P. P. Srinivasan, and P. Hedman, “Mip-nerf 360: Unbounded anti-aliased neural radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 5470–5479
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M. Cherti, R. Beaumont, R. Wightman, M. Wortsman, G. Ilharco, C. Gordon, C. Schuhmann, L. Schmidt, and J. Jitsev, “Reproducible scaling laws for contrastive language-image learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 2818–2829
2023
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Q. Yu, J. He, X. Deng, X. Shen, and L.-C. Chen, “Convolutions die hard: Open-vocabulary segmentation with single frozen convolutional clip,” in Proceedings of the Advances in Neural Information Processing Systems , 2023
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Y.-C. Guo, D. Kang, L. Bao, Y. He, and S.-H. Zhang, “Nerfren: Neural radiance fields with reflections,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 18 409–18 418
2022
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K. Wang, S. Peng, X. Zhou, J. Yang, and G. Zhang, “Nerfcap: Human performance capture with dynamic neural radiance fields,” IEEE Transactions on Visualization and Computer Graphics , 2022
2022
Cited alongside, same era.
V. Tschernezki, I. Laina, D. Larlus, and A. Vedaldi, “Neural feature fusion fields: 3d distillation of self-supervised 2d image representations,” in Proceedings of the International Conference on 3D Vision , 2022, pp. 443–453
2022
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Z. Fan, P. Wang, Y. Jiang, X. Gong, D. Xu, and Z. Wang, “Nerf-sos: Any-view self-supervised object segmentation on complex scenes,” in Proceedings of the International Conference on Learning Representations , 2022
2022
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S. Kobayashi, E. Matsumoto, and V. Sitzmann, “Decomposing nerf for editing via feature field distillation,” in Proceedings of the Advances in Neural Information Processing Systems , vol. 35, 2022, pp. 23 311–23 330
2022
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J.-B. Alayrac, J. Donahue, P. Luc, A. Miech, I. Barr, Y. Hasson, K. Lenc, A. Mensch, K. Millican, M. Reynolds et al. , “Flamingo: a visual language model for few-shot learning,” in Proceedings of the Advances in Neural Information Processing Systems , vol. 35, 2022, pp. 23 716–23 736
2022
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J. T. Barron, B. Mildenhall, D. Verbin, P. P. Srinivasan, and P. Hedman, “Zip-nerf: Anti-aliased grid-based neural radiance fields,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 19 697–19 705
2023
Cited alongside, same era.
K. Zhou, J.-X. Zhong, S. Shin, K. Lu, Y. Yang, A. Markham, and N. Trigoni, “Dynpoint: Dynamic neural point for view synthesis,” in Proceedings of the Advances in Neural Information Processing Systems , 2023
2023
Cited alongside, same era.
2023
Later among the works it cites.
F. Liang, B. Wu, X. Dai, K. Li, Y. Zhao, H. Zhang, P. Zhang, P. Vajda, and D. Marculescu, “Open-vocabulary semantic segmentation with mask-adapted clip,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 7061–7070
2023
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H. Zhang, F. Li, X. Zou, S. Liu, C. Li, J. Yang, and L. Zhang, “A simple framework for open-vocabulary segmentation and detection,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 1020–1031
2023
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J. Kerr, C. M. Kim, K. Goldberg, A. Kanazawa, and M. Tancik, “Lerf: Language embedded radiance fields,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 19 729–19 739
2023
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K. Liu, F. Zhan, J. Zhang, M. Xu, Y. Yu, A. El Saddik, C. Theobalt, E. Xing, and S. Lu, “Weakly supervised 3d open-vocabulary segmentation,” in Proceedings of the Advances in Neural Information Processing Systems , 2023, pp. 53 433–53 456
2023
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A. Kirillov, E. Mintun, N. Ravi, H. Mao, C. Rolland, L. Gustafson, T. Xiao, S. Whitehead, A. C. Berg, W.-Y. Lo, P. Dollar, and R. Girshick, “Segment anything,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 4015–4026
2023
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L. Song, A. Chen, Z. Li, Z. Chen, L. Chen, J. Yuan, Y. Xu, and A. Geiger, “Nerfplayer: A streamable dynamic scene representation with decomposed neural radiance fields,” IEEE Transactions on Visualization and Computer Graphics , vol. 29, no. 5, pp. 2732–2742, 2023
2023
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Y. Wei, S. Liu, J. Zhou, and J. Lu, “Depth-guided optimization of neural radiance fields for indoor multi-view stereo,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Later among the works it cites.
Z. Chen, C. Wang, Y.-C. Guo, and S.-H. Zhang, “Structnerf: Neural radiance fields for indoor scenes with structural hints,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
L. Ke, M. Ye, M. Danelljan, Y. Liu, Y.-W. Tai, C.-K. Tang, and F. Yu, “Segment anything in high quality,” in Proceedings of the Advances in Neural Information Processing Systems , 2023
2023
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S. Guo, Q. Wang, Y. Gao, R. Xie, L. Li, F. Zhu, and L. Song, “Depth-guided robust point cloud fusion nerf for sparse input views,” IEEE Transactions on Circuits and Systems for Video Technology , 2024
2024
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S. Wu, W. Zhang, L. Xu, S. Jin, X. Li, W. Liu, and C. C. Loy, “Clipself: Vision transformer distills itself for open-vocabulary dense prediction,” in Proceedings of the International Conference on Learning Representations , 2024
2024
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J. Ma, Y. He, F. Li, L. Han, C. You, and B. Wang, “Segment anything in medical images,” Nature Communications , vol. 15, no. 1, p. 654, 2024
2024
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