Fetching the paper…
Reading the bibliography…
The emergence of Neural Radiance Fields (NeRF) for novel view synthesis has increased interest in 3D scene editing.
L. Wang and C. Jung, “Example-based video stereolization with foreground segmentation and depth propagation,” IEEE Transactions on Multimedia , vol. 16, no. 7, pp. 1905–1914, 2014
2014
Earlier work this paper cites.
J. L. Schönberger, T. Price, T. Sattler, J.-M. Frahm, and M. Pollefeys, “A vote-and-verify strategy for fast spatial verification in image retrieval,” in Asian Conference on Computer Vision (ACCV) , 2016
2016
Earlier work this paper cites.
J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in Computer Vision – ECCV 2016 , B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds., 2016, pp. 694–711
2016
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “GANs trained by a two time-scale update rule converge to a local nash equilibrium,” in Proceedings of the 31st International Conference on Neural Information Processing Systems , ser. NIPS’17, 2017, pp. 6629–6640
2017
Earlier work this paper cites.
Z. Qiu, T. Yao, and T. Mei, “Learning deep spatio-temporal dependence for semantic video segmentation,” IEEE Transactions on Multimedia , vol. 20, no. 4, pp. 939–949, 2018
2018
Earlier work this paper cites.
A. H. Abdulnabi, B. Shuai, Z. Zuo, L.-P. Chau, and G. Wang, “Multimodal recurrent neural networks with information transfer layers for indoor scene labeling,” IEEE Transactions on Multimedia , vol. 20, no. 7, pp. 1656–1671, 2018
2018
Earlier work this paper cites.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The Unreasonable Effectiveness of Deep Features as a Perceptual Metric,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 586–595
2018
Earlier work this paper cites.
B. Mildenhall, P. P. Srinivasan, R. Ortiz-Cayon, N. K. Kalantari, R. Ramamoorthi, R. Ng, and A. Kar, “Local light field fusion: Practical view synthesis with prescriptive sampling guidelines,” ACM Transactions on Graphics (TOG) , 2019
2019
Earlier work this paper cites.
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,” in Computer Vision – ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part I , 2020, pp. 405–421. [Online]. Available: https://doi.org/10.1007/978-3-030-58452-8_24
2020
Earlier work this paper cites.
F. Xiang, Z. Xu, M. Hašan, Y. Hold-Geoffroy, K. Sunkavalli, and H. Su, “NeuTex: Neural Texture Mapping for Volumetric Neural Rendering,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 7115–7124
2021
Earlier work this paper cites.
B. Yang, Y. Zhang, Y. Xu, Y. Li, H. Zhou, H. Bao, G. Zhang, and Z. Cui, “Learning Object-Compositional Neural Radiance Field for Editable Scene Rendering,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 13 759–13 768
2021
Earlier work this paper cites.
Y. Hao, Y. Liu, Z. Wu, L. Han, Y. Chen, G. Chen, L. Chu, S. Tang, Z. Yu, Z. Chen, and B. Lai, “EdgeFlow: Achieving Practical Interactive Segmentation with Edge-Guided Flow,” in 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW) , 2021, pp. 1551–1560
2021
Earlier work this paper cites.
M. Caron, H. Touvron, I. Misra, H. Jegou, J. Mairal, P. Bojanowski, and A. Joulin, “Emerging Properties in Self-Supervised Vision Transformers,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 9630–9640
2021
Earlier work this paper cites.
S. Zhi, T. Laidlow, S. Leutenegger, and A. J. Davison, “In-Place Scene Labelling and Understanding with Implicit Scene Representation,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 15 818–15 827
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever, “Learning Transferable Visual Models From Natural Language Supervision,” in Proceedings of the 38th International Conference on Machine Learning , 2021, pp. 8748–8763. [Online]. Available: https://proceedings.mlr.press/v139/radford21a.html
2021
Earlier work this paper cites.
K. Rematas, R. Martin-Brualla, and V. Ferrari, “Sharf: Shape-conditioned Radiance Fields from a Single View,” in Proceedings of the 38th International Conference on Machine Learning , 2021, pp. 8948–8958. [Online]. Available: https://proceedings.mlr.press/v139/rematas21a.html
2021
Earlier work this paper cites.
S. Liu, X. Zhang, Z. Zhang, R. Zhang, J.-Y. Zhu, and B. Russell, “Editing Conditional Radiance Fields,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV) , 2021, pp. 5753–5763
2021
Earlier work this paper cites.
Q. Wang, Z. Wang, K. Genova, P. Srinivasan, H. Zhou, J. T. Barron, R. Martin-Brualla, N. Snavely, and T. Funkhouser, “Ibrnet: Learning multi-view image-based rendering,” in CVPR , 2021
2021
Earlier work this paper cites.
Y. Peng, Y. Yan, S. Liu, Y. Cheng, S. Guan, B. Pan, G. Zhai, and X. Yang, “CageNeRF: Cage-based Neural Radiance Field for Generalized 3D Deformation and Animation,” in Advances in Neural Information Processing Systems , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds., vol. 35, 2022, pp. 31 402–31 415. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2022/file/cb78e6b5246b03e0b82b4acc8b11cc21-Paper-Conference.pdf
2022
Earlier work this paper cites.
T. Xu and T. Harada, “Deforming Radiance Fields with Cages,” in Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXIII , 2022, pp. 159–175. [Online]. Available: https://doi.org/10.1007/978-3-031-19827-4_10
2022
Earlier work this paper cites.
Y.-J. Yuan, Y.-T. Sun, Y.-K. Lai, Y. Ma, R. Jia, and L. Gao, “NeRF-Editing: Geometry Editing of Neural Radiance Fields,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 18 332–18 343
2022
Earlier work this paper cites.
B. Yang, C. Bao, J. Zeng, H. Bao, Y. Zhang, Z. Cui, and G. Zhang, “NeuMesh: Learning Disentangled Neural Mesh-Based Implicit Field for Geometry and Texture Editing,” in Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XVI , 2022, pp. 597–614. [Online]. Available: https://doi.org/10.1007/978-3-031-19787-1_34
2022
Cited alongside, same era.
Q. Wu, X. Liu, Y. Chen, K. Li, C. Zheng, J. Cai, and J. Zheng, “Object-Compositional Neural Implicit Surfaces,” in Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXVII , 2022, pp. 197–213. [Online]. Available: https://doi.org/10.1007/978-3-031-19812-0_12
2022
Cited alongside, same era.
2022
Cited alongside, same era.
T. Zhou, F. Porikli, D. J. Crandall, L. Van Gool, and W. Wang, “A survey on deep learning technique for video segmentation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 45, no. 6, pp. 7099–7122, 2023
2023
Closest in time.
2023
Closest in time.
L. Zhao, H. Zhou, X. Zhu, X. Song, H. Li, and W. Tao, “Lif-seg: Lidar and camera image fusion for 3d lidar semantic segmentation,” IEEE Transactions on Multimedia , pp. 1–11, 2023
2023
Closest in time.
M. Wallingford, A. Kusupati, A. Fang, V. Ramanujan, A. Kembhavi, R. Mottaghi, and A. Farhadi, “Neural Radiance Field Codebooks,” in ICLR , 2023. [Online]. Available: https://openreview.net/forum?id=mX56bKDybu5
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
R. Suvorov, E. Logacheva, A. Mashikhin, A. Remizova, A. Ashukha, A. Silvestrov, N. Kong, H. Goka, K. Park, and V. Lempitsky, “Resolution-robust Large Mask Inpainting with Fourier Convolutions,” in 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) , 2022, pp. 3172–3182
2022
Cited alongside, same era.
S. Kobayashi, E. Matsumoto, and V. Sitzmann, “Decomposing NeRF for Editing via Feature Field Distillation,” in Advances in Neural Information Processing Systems , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds., vol. 35, 2022, pp. 23 311–23 330. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2022/file/93f250215e4889119807b6fac3a57aec-Paper-Conference.pdf
2022
Cited alongside, same era.
A. Chen, Z. Xu, A. Geiger, J. Yu, and H. Su, “TensoRF: Tensorial Radiance Fields,” in Computer Vision – ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXII , 2022, pp. 333–350. [Online]. Available: https://doi.org/10.1007/978-3-031-19824-3_20
2022
Cited alongside, same era.
2022
Cited alongside, same era.
X. Liu, J. Chen, H. Yu, Y.-W. Tai, and C.-K. Tang, “Unsupervised Multi-View Object Segmentation Using Radiance Field Propagation,” in Advances in Neural Information Processing Systems , S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds., vol. 35, 2022, pp. 17 730–17 743. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2022/file/70de9e3948645a1be2de657f14d85c6d-Paper-Conference.pdf
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Z. Chen, K. Yin, and S. Fidler, “AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 1455–1464
2022
Cited alongside, same era.
H.-X. Yu, L. Guibas, and J. Wu, “Unsupervised Discovery of Object Radiance Fields,” in ICLR , 2022. [Online]. Available: https://openreview.net/forum?id=rwE8SshAlxw
2022
Cited alongside, same era.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
B. Wang, L. Chen, and B. Yang, “DM-NeRF: 3D Scene Geometry Decomposition and Manipulation from 2D Images,” in ICLR , 2023. [Online]. Available: https://openreview.net/forum?id=C_PRLz8bEJx
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
IDEA-Research, “Grounded-sam,” https://github.com/IDEA-Research/Grounded-Segment-Anything
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
B. Poole, A. Jain, J. T. Barron, and B. Mildenhall, “DreamFusion: Text-to-3D using 2D Diffusion,” in ICLR , 2023. [Online]. Available: https://openreview.net/forum?id=FjNys5c7VyY
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
S. Cao, W. Chai, S. Hao, Y. Zhang, H. Chen, and G. Wang, “Difffashion: Reference-based fashion design with structure-aware transfer by diffusion models,” IEEE Transactions on Multimedia , pp. 1–13, 2023
2023
Closest in time.