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Despite significant strides in the field of 3D scene editing, current methods encounter substantial challenge, particularly in preserving 3D consistency in multi-view editing process.
2020
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B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021
2021
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P. Wang, L. Liu, Y. Liu, C. Theobalt, T. Komura, and W. Wang, “Neus: Learning neural implicit surfaces by volume rendering for multi-view reconstruction,” in Adv. Neural Inform. Process. Syst. , 2021, pp. 27 171–27 183
2021
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Q. Meng, W. Wang, T. Zhou, J. Shen, Y. Jia, and L. Van Gool, “Towards a weakly supervised framework for 3d point cloud object detection and annotation,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 44, no. 8, pp. 4454–4468, 2021
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R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2022, pp. 10 684–10 695
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A. Graikos, N. Malkin, N. Jojic, and D. Samaras, “Diffusion Models as Plug-and-Play Priors.” in Adv. Neural Inform. Process. Syst. , 2022
2022
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A. Hertz, R. Mokady, J. Tenenbaum, K. Aberman, Y. Pritch, and D. Cohen-Or, “Prompt-to-Prompt Image Editing with Cross-Attention Control,” in Int. Conf. Learn. Represent. , 2022
2022
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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 IEEE Conf. Comput. Vis. Pattern Recog. , 2022, pp. 5470–5479
2022
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T. Feng, W. Wang, X. Wang, Y. Yang, and Q. Zheng, “Clustering based point cloud representation learning for 3d analysis,” in Int. Conf. Comput. Vis. , 2023, pp. 8283–8294
2023
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B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering,” ACM Trans. Graph. , vol. 42, no. 4, pp. 1–14, 2023
2023
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T. Brooks, A. Holynski, and A. A. Efros, “Instructpix2pix: Learning to Follow Image Editing Instructions,” in IEEE Conf. Comput. Vis. Pattern Recog. IEEE, 2023, pp. 18 392–18 402
2023
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A. Haque, M. Tancik, A. A. Efros, A. Holynski, and A. Kanazawa, “Instruct-nerf2nerf: Editing 3d scenes with instructions,” in Int. Conf. Comput. Vis. , October 2023, pp. 19 740–19 750
2023
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2023
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2023
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2023
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2023
Cited alongside, same era.
E. Sella, G. Fiebelman, P. Hedman, and H. Averbuch-Elor, “Vox-e: Text-guided voxel editing of 3d objects,” in Int. Conf. Comput. Vis. , 2023, pp. 430–440
2023
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J. Zhuang, C. Wang, L. Lin, L. Liu, and G. Li, “Dreameditor: Text-driven 3d scene editing with neural fields,” ACM Trans. Graph. , 2023
2023
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B. Poole, A. Jain, J. T. Barron, and B. Mildenhall, “Dreamfusion: Text-to-3d using 2d Diffusion,” in Int. Conf. Learn. Represent. , 2023
2023
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A. Hertz, K. Aberman, and D. Cohen-Or, “Delta Denoising Score,” in Int. Conf. Comput. Vis. IEEE, 2023, pp. 2328–2337
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2024
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M. Chen, I. Laina, and A. Vedaldi, “Dge: Direct gaussian 3d editing by consistent multi-view editing,” in Eur. Conf. Comput. Vis. , 2024
2024
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2024
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2024
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J. Wu, J.-W. Bian, X. Li, G. Wang, I. Reid, P. Torr, and V. A. Prisacariu, “Gaussctrl: Multi-view consistent text-driven 3d gaussian splatting editing,” in Eur. Conf. Comput. Vis. , 2024
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2023
Cited alongside, same era.
X. Zhou, Y. He, F. R. Yu, J. Li, and Y. Li, “Repaint-nerf: Nerf editting via semantic masks and diffusion models,” in Int. Joint Conf. on Artificial Intell. , 2023, pp. 1813–1821
2023
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2023
Cited alongside, same era.
J. Dong and Y.-X. Wang, “Vica-NeRF: View-Consistency-Aware 3d Editing of Neural Radiance Fields.” in Adv. Neural Inform. Process. Syst. , 2023
2023
Cited alongside, same era.
H. Wang, X. Du, J. Li, R. A. Yeh, and G. Shakhnarovich, “Score Jacobian Chaining: Lifting Pretrained 2d Diffusion Models for 3d Generation,” in IEEE Conf. Comput. Vis. Pattern Recog. IEEE, 2023, pp. 12 619–12 629
2023
Cited alongside, same era.
2023
Cited alongside, same era.
A. Haque, M. Tancik, A. A. Efros, A. Holynski, and A. Kanazawa, “Instruct-NeRF2NeRF: Editing 3d Scenes with Instructions.” in Int. Conf. Comput. Vis. , 2023, pp. 19 683–19 693
2023
Cited alongside, same era.
J. Zhu, P. Zhuang, and S. Koyejo, “HIFA: High-fidelity text-to-3d generation with advanced diffusion guidance,” in Int. Conf. Learn. Represent. , 2024
2024
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2024
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J. Koo, C. Park, and M. Sung, “Posterior distillation sampling,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2024
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Y. Li, Y. Dou, Y. Shi, Y. Lei, X. Chen, Y. Zhang, P. Zhou, and B. Ni, “Focaldreamer: Text-driven 3d editing via focal-fusion assembly,” in AAAI Conf. on Artificial Intell. , 2024
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J. Park, G. Kwon, and J. C. Ye, “Ed-nerf: Efficient text-guided editing of 3d scene with latent space nerf,” in Int. Conf. Learn. Represent. , 2024
2024
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Y. Huang, J. Wang, Y. Shi, X. Qi, Z.-J. Zha, and L. Zhang, “Dreamtime: An Improved Optimization Strategy for Text-to-3d Content Creation,” in Int. Conf. Learn. Represent. , 2024
2024
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Y. Chen, Z. Chen, C. Zhang, F. Wang, X. Yang, Y. Wang, Z. Cai, L. Yang, H. Liu, and G. Lin, “Gaussianeditor: Swift and controllable 3d editing with gaussian splatting,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2024
2024
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S. Xu, Y. Huang, J. Pan, Z. Ma, and J. Chai, “Inversion-Free Image Editing with Natural Language,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2024
2024
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J.-K. Chen, S. R. Bulò, N. Müller, L. Porzi, P. Kontschieder, and Y.-X. Wang, “Consistdreamer: 3d-Consistent 2d Diffusion for High-Fidelity Scene Editing,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2024
2024
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X. Yi, Z. Wu, Q. Xu, P. Zhou, J.-H. Lim, and H. Zhang, “Diffusion Time-step Curriculum for One Image to 3d Generation,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2024
2024
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O. Katzir, O. Patashnik, D. Cohen-Or, and D. Lischinski, “Noise-free Score Distillation,” in Int. Conf. Learn. Represent. , 2024
2024
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