Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
149 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review (2210.00379v5)

Published 1 Oct 2022 in cs.CV

Abstract: Neural Radiance Field (NeRF) has recently become a significant development in the field of Computer Vision, allowing for implicit, neural network-based scene representation and novel view synthesis. NeRF models have found diverse applications in robotics, urban mapping, autonomous navigation, virtual reality/augmented reality, and more. Due to the growing popularity of NeRF and its expanding research area, we present a comprehensive survey of NeRF papers from the past two years. Our survey is organized into architecture and application-based taxonomies and provides an introduction to the theory of NeRF and its training via differentiable volume rendering. We also present a benchmark comparison of the performance and speed of key NeRF models. By creating this survey, we hope to introduce new researchers to NeRF, provide a helpful reference for influential works in this field, as well as motivate future research directions with our discussion section.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (191)
  1. 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 European conference on computer vision.   Springer, 2020, pp. 405–421.
  2. J. L. Schonberger and J.-M. Frahm, “Structure-from-motion revisited,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 4104–4113.
  3. M. Levoy and P. Hanrahan, “Light field rendering,” in Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, 1996, pp. 31–42.
  4. S. J. Gortler, R. Grzeszczuk, R. Szeliski, and M. F. Cohen, “The lumigraph,” in Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, 1996, pp. 43–54.
  5. 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), vol. 38, no. 4, pp. 1–14, 2019.
  6. V. Sitzmann, M. Zollhöfer, and G. Wetzstein, “Scene representation networks: Continuous 3d-structure-aware neural scene representations,” Advances in Neural Information Processing Systems, vol. 32, 2019.
  7. S. Lombardi, T. Simon, J. Saragih, G. Schwartz, A. Lehrmann, and Y. Sheikh, “Neural volumes: learning dynamic renderable volumes from images,” ACM Transactions on Graphics (TOG), vol. 38, no. 4, pp. 1–14, 2019.
  8. M. Niemeyer, L. Mescheder, M. Oechsle, and A. Geiger, “Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 3504–3515.
  9. K. Genova, F. Cole, A. Sud, A. Sarna, and T. Funkhouser, “Local deep implicit functions for 3d shape,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 4857–4866.
  10. J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove, “Deepsdf: Learning continuous signed distance functions for shape representation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 165–174.
  11. F. Dellaert and L. Yen-Chen, “Neural volume rendering: Nerf and beyond,” arXiv preprint arXiv:2101.05204, 2020.
  12. Y. Xie, T. Takikawa, S. Saito, O. Litany, S. Yan, N. Khan, F. Tombari, J. Tompkin, V. Sitzmann, and S. Sridhar, “Neural fields in visual computing and beyond,” in Computer Graphics Forum, vol. 41, no. 2.   Wiley Online Library, 2022, pp. 641–676.
  13. F. Zhan, Y. Yu, R. Wu, J. Zhang, and S. Lu, “Multimodal image synthesis and editing: A survey,” arXiv preprint arXiv:2112.13592, 2021.
  14. C. Wang, M. Chai, M. He, D. Chen, and J. Liao, “Clip-nerf: Text-and-image driven manipulation of neural radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 3835–3844.
  15. Y. Guo, K. Chen, S. Liang, Y.-J. Liu, H. Bao, and J. Zhang, “Ad-nerf: Audio driven neural radiance fields for talking head synthesis,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 5784–5794.
  16. A. Jain, B. Mildenhall, J. T. Barron, P. Abbeel, and B. Poole, “Zero-shot text-guided object generation with dream fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 867–876.
  17. K. Jo, G. Shim, S. Jung, S. Yang, and J. Choo, “Cg-nerf: Conditional generative neural radiance fields,” IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023.
  18. J. Sun, X. Wang, Y. Shi, L. Wang, J. Wang, and Y. Liu, “Ide-3d: Interactive disentangled editing for high-resolution 3d-aware portrait synthesis,” arXiv preprint arXiv:2205.15517, 2022.
  19. Y. Chen, Q. Wu, C. Zheng, T.-J. Cham, and J. Cai, “Sem2nerf: Converting single-view semantic masks to neural radiance fields,” European conference on computer vision, 2022.
  20. A. Tewari, J. Thies, B. Mildenhall, P. Srinivasan, E. Tretschk, W. Yifan, C. Lassner, V. Sitzmann, R. Martin-Brualla, S. Lombardi et al., “Advances in neural rendering,” in Computer Graphics Forum, vol. 41, no. 2.   Wiley Online Library, 2022, pp. 703–735.
  21. J. T. Kajiya and B. P. Von Herzen, “Ray tracing volume densities,” ACM SIGGRAPH computer graphics, vol. 18, no. 3, pp. 165–174, 1984.
  22. M. Niemeyer, J. T. Barron, B. Mildenhall, M. S. Sajjadi, A. Geiger, and N. Radwan, “Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 5480–5490.
  23. K. Deng, A. Liu, J.-Y. Zhu, and D. Ramanan, “Depth-supervised nerf: Fewer views and faster training for free,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12 882–12 891.
  24. 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.
  25. D. Xu, Y. Jiang, P. Wang, Z. Fan, H. Shi, and Z. Wang, “Sinnerf: Training neural radiance fields on complex scenes from a single image,” 2022.
  26. M. Tancik, P. Srinivasan, B. Mildenhall, S. Fridovich-Keil, N. Raghavan, U. Singhal, R. Ramamoorthi, J. Barron, and R. Ng, “Fourier features let networks learn high frequency functions in low dimensional domains,” Advances in Neural Information Processing Systems, vol. 33, pp. 7537–7547, 2020.
  27. S. Ramasinghe and S. Lucey, “Beyond periodicity: towards a unifying framework for activations in coordinate-mlps,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXIII.   Springer, 2022, pp. 142–158.
  28. R. Jensen, A. Dahl, G. Vogiatzis, E. Tola, and H. Aanæs, “Large scale multi-view stereopsis evaluation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, pp. 406–413.
  29. A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 5828–5839.
  30. A. Dai, M. Nießner, M. Zollöfer, S. Izadi, and C. Theobalt, “Bundlefusion: Real-time globally consistent 3d reconstruction using on-the-fly surface re-integration,” ACM Transactions on Graphics 2017 (TOG), 2017.
  31. A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su et al., “Shapenet: An information-rich 3d model repository,” arXiv preprint arXiv:1512.03012, 2015.
  32. A. Knapitsch, J. Park, Q.-Y. Zhou, and V. Koltun, “Tanks and temples: Benchmarking large-scale scene reconstruction,” ACM Transactions on Graphics (ToG), vol. 36, no. 4, pp. 1–13, 2017.
  33. A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Niessner, M. Savva, S. Song, A. Zeng, and Y. Zhang, “Matterport3d: Learning from rgb-d data in indoor environments,” arXiv preprint arXiv:1709.06158, 2017.
  34. J. Straub, T. Whelan, L. Ma, Y. Chen, E. Wijmans, S. Green, J. J. Engel, R. Mur-Artal, C. Ren, S. Verma et al., “The replica dataset: A digital replica of indoor spaces,” arXiv preprint arXiv:1906.05797, 2019.
  35. B. Deng, J. T. Barron, and P. P. Srinivasan, “JaxNeRF: an efficient JAX implementation of NeRF,” 2020. [Online]. Available: https://github.com/google-research/google-research/tree/master/jaxnerf
  36. L. Liu, J. Gu, K. Z. Lin, T.-S. Chua, and C. Theobalt, “Neural sparse voxel fields,” NeurIPS, 2020.
  37. P. Hedman, P. P. Srinivasan, B. Mildenhall, J. T. Barron, and P. Debevec, “Baking neural radiance fields for real-time view synthesis,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 5875–5884.
  38. A. Yu, R. Li, M. Tancik, H. Li, R. Ng, and A. Kanazawa, “PlenOctrees for real-time rendering of neural radiance fields,” in ICCV, 2021.
  39. S. J. Garbin, M. Kowalski, M. Johnson, J. Shotton, and J. Valentin, “Fastnerf: High-fidelity neural rendering at 200fps,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 14 346–14 355.
  40. C. Reiser, S. Peng, Y. Liao, and A. Geiger, “Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 14 335–14 345.
  41. T. Müller, A. Evans, C. Schied, and A. Keller, “Instant neural graphics primitives with a multiresolution hash encoding,” ACM Trans. Graph., vol. 41, no. 4, pp. 102:1–102:15, Jul. 2022. [Online]. Available: https://doi.org/10.1145/3528223.3530127
  42. A. Yu, S. Fridovich-Keil, M. Tancik, Q. Chen, B. Recht, and A. Kanazawa, “Plenoxels: Radiance fields without neural networks,” arXiv preprint arXiv:2112.05131, 2021.
  43. C. Sun, M. Sun, and H.-T. Chen, “Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 5459–5469.
  44. 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.   Springer, 2022, pp. 333–350.
  45. 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.
  46. D. Verbin, P. Hedman, B. Mildenhall, T. Zickler, J. T. Barron, and P. P. Srinivasan, “Ref-nerf: Structured view-dependent appearance for neural radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 5491–5500.
  47. A. Chen, Z. Xu, F. Zhao, X. Zhang, F. Xiang, J. Yu, and H. Su, “Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 14 124–14 133.
  48. A. Jain, M. Tancik, and P. Abbeel, “Putting nerf on a diet: Semantically consistent few-shot view synthesis,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 5885–5894.
  49. J. Li, Z. Feng, Q. She, H. Ding, C. Wang, and G. H. Lee, “Mine: Towards continuous depth mpi with nerf for novel view synthesis,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 12 578–12 588.
  50. A. Kundu, K. Genova, X. Yin, A. Fathi, C. Pantofaru, L. J. Guibas, A. Tagliasacchi, F. Dellaert, and T. Funkhouser, “Panoptic neural fields: A semantic object-aware neural scene representation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12 871–12 881.
  51. A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in Conference on Computer Vision and Pattern Recognition (CVPR), 2012.
  52. A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research, vol. 32, no. 11, pp. 1231–1237, 2013.
  53. J. Fritsch, T. Kuehnl, and A. Geiger, “A new performance measure and evaluation benchmark for road detection algorithms,” in International Conference on Intelligent Transportation Systems (ITSC), 2013.
  54. M. Menze and A. Geiger, “Object scene flow for autonomous vehicles,” in Conference on Computer Vision and Pattern Recognition (CVPR), 2015.
  55. Y. Liao, J. Xie, and A. Geiger, “Kitti-360: A novel dataset and benchmarks for urban scene understanding in 2d and 3d,” IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.
  56. P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V. Patnaik, P. Tsui, J. Guo, Y. Zhou, Y. Chai, B. Caine et al., “Scalability in perception for autonomous driving: Waymo open dataset,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 2446–2454.
  57. K. Park, U. Sinha, J. T. Barron, S. Bouaziz, D. B. Goldman, S. M. Seitz, and R. Martin-Brualla, “Nerfies: Deformable neural radiance fields,” ICCV, 2021.
  58. K. Park, U. Sinha, P. Hedman, J. T. Barron, S. Bouaziz, D. B. Goldman, R. Martin-Brualla, and S. M. Seitz, “Hypernerf: A higher-dimensional representation for topologically varying neural radiance fields,” ACM Trans. Graph., vol. 40, no. 6, dec 2021.
  59. S. Peng, Y. Zhang, Y. Xu, Q. Wang, Q. Shuai, H. Bao, and X. Zhou, “Neural body: Implicit neural representations with structured latent codes for novel view synthesis of dynamic humans,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 9054–9063.
  60. W. Jiang, K. M. Yi, G. Samei, O. Tuzel, and A. Ranjan, “Neuman: Neural human radiance field from a single video,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXII.   Springer, 2022, pp. 402–418.
  61. H. Joo, H. Liu, L. Tan, L. Gui, B. Nabbe, I. Matthews, T. Kanade, S. Nobuhara, and Y. Sheikh, “Panoptic studio: A massively multiview system for social motion capture,” in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 3334–3342.
  62. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing, vol. 13, no. 4, pp. 600–612, 2004.
  63. R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 586–595.
  64. F. N. Iandola, S. Han, M. W. Moskewicz, K. Ashraf, W. J. Dally, and K. Keutzer, “Squeezenet: Alexnet-level accuracy with 50x fewer parameters and¡ 0.5 mb model size,” arXiv preprint arXiv:1602.07360, 2016.
  65. K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv preprint arXiv:1409.1556, 2014.
  66. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems, vol. 25, 2012.
  67. B. Mildenhall, P. Hedman, R. Martin-Brualla, P. P. Srinivasan, and J. T. Barron, “Nerf in the dark: High dynamic range view synthesis from noisy raw images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16 190–16 199.
  68. B. Roessle, J. T. Barron, B. Mildenhall, P. P. Srinivasan, and M. Nießner, “Dense depth priors for neural radiance fields from sparse input views,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12 892–12 901.
  69. Y. Wei, S. Liu, Y. Rao, W. Zhao, J. Lu, and J. Zhou, “Nerfingmvs: Guided optimization of neural radiance fields for indoor multi-view stereo,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 5610–5619.
  70. K. Rematas, A. Liu, P. P. Srinivasan, J. T. Barron, A. Tagliasacchi, T. Funkhouser, and V. Ferrari, “Urban radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12 932–12 942.
  71. Q. Xu, Z. Xu, J. Philip, S. Bi, Z. Shu, K. Sunkavalli, and U. Neumann, “Point-nerf: Point-based neural radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 5438–5448.
  72. D. B. Lindell, J. N. Martel, and G. Wetzstein, “Autoint: Automatic integration for fast neural volume rendering,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 14 556–14 565.
  73. A. Yu, V. Ye, M. Tancik, and A. Kanazawa, “pixelNeRF: Neural radiance fields from one or few images,” in CVPR, 2021.
  74. Y. Liu, S. Peng, L. Liu, Q. Wang, P. Wang, C. Theobalt, X. Zhou, and W. Wang, “Neural rays for occlusion-aware image-based rendering,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 7824–7833.
  75. M. Niemeyer and A. Geiger, “Giraffe: Representing scenes as compositional generative neural feature fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 11 453–11 464.
  76. K. Schwarz, Y. Liao, M. Niemeyer, and A. Geiger, “Graf: Generative radiance fields for 3d-aware image synthesis,” Advances in Neural Information Processing Systems, vol. 33, pp. 20 154–20 166, 2020.
  77. E. R. Chan, M. Monteiro, P. Kellnhofer, J. Wu, and G. Wetzstein, “pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 5799–5809.
  78. Q. Meng, A. Chen, H. Luo, M. Wu, H. Su, L. Xu, X. He, and J. Yu, “Gnerf: Gan-based neural radiance field without posed camera,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 6351–6361.
  79. J. Gu, L. Liu, P. Wang, and C. Theobalt, “Stylenerf: A style-based 3d aware generator for high-resolution image synthesis,” in Tenth International Conference on Learning Representations, 2022, pp. 1–25.
  80. E. R. Chan, C. Z. Lin, M. A. Chan, K. Nagano, B. Pan, S. De Mello, O. Gallo, L. J. Guibas, J. Tremblay, S. Khamis et al., “Efficient geometry-aware 3d generative adversarial networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16 123–16 133.
  81. B. Poole, A. Jain, J. T. Barron, and B. Mildenhall, “Dreamfusion: Text-to-3d using 2d diffusion,” arXiv preprint arXiv:2209.14988, 2022.
  82. C.-H. Lin, J. Gao, L. Tang, T. Takikawa, X. Zeng, X. Huang, K. Kreis, S. Fidler, M.-Y. Liu, and T.-Y. Lin, “Magic3d: High-resolution text-to-3d content creation,” arXiv preprint arXiv:2211.10440, 2022.
  83. L. Melas-Kyriazi, C. Rupprecht, I. Laina, and A. Vedaldi, “Realfusion: 360° reconstruction of any object from a single image,” arXiv e-prints, pp. arXiv–2302, 2023.
  84. R. Martin-Brualla, N. Radwan, M. S. M. Sajjadi, J. T. Barron, A. Dosovitskiy, and D. Duckworth, “NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections,” in CVPR, 2021.
  85. S. Liu, X. Zhang, Z. Zhang, R. Zhang, J.-Y. Zhu, and B. Russell, “Editing conditional radiance fields,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 5773–5783.
  86. K. Zhang, G. Riegler, N. Snavely, and V. Koltun, “Nerf++: Analyzing and improving neural radiance fields,” arXiv:2010.07492, 2020.
  87. C. Xie, K. Park, R. Martin-Brualla, and M. Brown, “Fig-nerf: Figure-ground neural radiance fields for 3d object category modelling,” in 2021 International Conference on 3D Vision (3DV).   IEEE, 2021, pp. 962–971.
  88. 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 Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 13 779–13 788.
  89. S. Vora*, N. Radwan*, K. Greff, H. Meyer, K. Genova, M. S. M. Sajjadi, E. Pot, A. Tagliasacchi, and D. Duckworth, “Neural semantic fields for generalizable semantic segmentation of 3d scenes,” Transactions on Machine Learning Research, 2022, https://openreview.net/forum?id=ggPhsYCsm9.
  90. S. Zhi, T. Laidlow, S. Leutenegger, and A. J. Davison, “In-place scene labelling and understanding with implicit scene representation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 15 838–15 847.
  91. E. Sucar, S. Liu, J. Ortiz, and A. J. Davison, “imap: Implicit mapping and positioning in real-time,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 6229–6238.
  92. Z. Zhu, S. Peng, V. Larsson, W. Xu, H. Bao, Z. Cui, M. R. Oswald, and M. Pollefeys, “Nice-slam: Neural implicit scalable encoding for slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12 786–12 796.
  93. A. Rosinol, J. J. Leonard, and L. Carlone, “Nerf-slam: Real-time dense monocular slam with neural radiance fields,” arXiv preprint arXiv:2210.13641, 2022.
  94. Z. Wang, S. Wu, W. Xie, M. Chen, and V. A. Prisacariu, “NeRF−⁣−--- -: Neural radiance fields without known camera parameters,” arXiv preprint arXiv:2102.07064, 2021.
  95. C.-H. Lin, W.-C. Ma, A. Torralba, and S. Lucey, “Barf: Bundle-adjusting neural radiance fields,” in IEEE International Conference on Computer Vision (ICCV), 2021.
  96. Y. Jeong, S. Ahn, C. Choy, A. Anandkumar, M. Cho, and J. Park, “Self-calibrating neural radiance fields,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 5846–5854.
  97. S.-F. Chng, S. Ramasinghe, J. Sherrah, and S. Lucey, “Gaussian activated neural radiance fields for high fidelity reconstruction and pose estimation,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXIII.   Springer, 2022, pp. 264–280.
  98. 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.
  99. J. Zhang, Y. Zhang, H. Fu, X. Zhou, B. Cai, J. Huang, R. Jia, B. Zhao, and X. Tang, “Ray priors through reprojection: Improving neural radiance fields for novel view extrapolation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 18 376–18 386.
  100. X. Cheng, P. Wang, and R. Yang, “Learning depth with convolutional spatial propagation network,” IEEE transactions on pattern analysis and machine intelligence, vol. 42, no. 10, pp. 2361–2379, 2019.
  101. Z. Li, T. Dekel, F. Cole, R. Tucker, N. Snavely, C. Liu, and W. T. Freeman, “Learning the depths of moving people by watching frozen people,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 4521–4530.
  102. Y. Yao, Z. Luo, S. Li, T. Fang, and L. Quan, “Mvsnet: Depth inference for unstructured multi-view stereo,” in Proceedings of the European conference on computer vision (ECCV), 2018, pp. 767–783.
  103. C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 652–660.
  104. E. Insafutdinov, D. Campbell, J. F. Henriques, and A. Vedaldi, “Snes: Learning probably symmetric neural surfaces from incomplete data,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXII.   Springer, 2022, pp. 367–383.
  105. W. Yang, G. Chen, C. Chen, Z. Chen, and K.-Y. K. Wong, “S33{}^{3}start_FLOATSUPERSCRIPT 3 end_FLOATSUPERSCRIPT-nerf: Neural reflectance field from shading and shadow under a single viewpoint,” in Advances in Neural Information Processing Systems, 2022.
  106. J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang, “JAX: composable transformations of Python+NumPy programs,” 2018. [Online]. Available: http://github.com/google/jax
  107. S. G. Parker, J. Bigler, A. Dietrich, H. Friedrich, J. Hoberock, D. Luebke, D. McAllister, M. McGuire, K. Morley, A. Robison et al., “Optix: a general purpose ray tracing engine,” Acm transactions on graphics (tog), vol. 29, no. 4, pp. 1–13, 2010.
  108. L. Wang, J. Zhang, X. Liu, F. Zhao, Y. Zhang, Y. Zhang, M. Wu, J. Yu, and L. Xu, “Fourier plenoctrees for dynamic radiance field rendering in real-time,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 13 524–13 534.
  109. Z. Chen, T. Funkhouser, P. Hedman, and A. Tagliasacchi, “Mobilenerf: Exploiting the polygon rasterization pipeline for efficient neural field rendering on mobile architectures,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.
  110. T. Hu, S. Liu, Y. Chen, T. Shen, and J. Jia, “Efficientnerf efficient neural radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12 902–12 911.
  111. L. Wu, J. Y. Lee, A. Bhattad, Y.-X. Wang, and D. Forsyth, “Diver: Real-time and accurate neural radiance fields with deterministic integration for volume rendering,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 16 200–16 209.
  112. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 770–778.
  113. A. Trevithick and B. Yang, “Grf: Learning a general radiance field for 3d representation and rendering,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 15 182–15 192.
  114. A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al., “Learning transferable visual models from natural language supervision,” in International Conference on Machine Learning.   PMLR, 2021, pp. 8748–8763.
  115. M. M. Johari, Y. Lepoittevin, and F. Fleuret, “Geonerf: Generalizing nerf with geometry priors,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 18 365–18 375.
  116. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
  117. D. Rebain, M. Matthews, K. M. Yi, D. Lagun, and A. Tagliasacchi, “Lolnerf: Learn from one look,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 1558–1567.
  118. P. Bojanowski, A. Joulin, D. Lopez-Paz, and A. Szlam, “Optimizing the latent space of generative networks,” arXiv preprint arXiv:1707.05776, 2017.
  119. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems, vol. 27, 2014.
  120. J. Chibane, A. Bansal, V. Lazova, and G. Pons-Moll, “Stereo radiance fields (srf): Learning view synthesis for sparse views of novel scenes,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 7911–7920.
  121. X. Zhang, S. Bi, K. Sunkavalli, H. Su, and Z. Xu, “Nerfusion: Fusing radiance fields for large-scale scene reconstruction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 5449–5458.
  122. K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, 2014.
  123. N. Müller, A. Simonelli, L. Porzi, S. R. Bulò, M. Nießner, and P. Kontschieder, “Autorf: Learning 3d object radiance fields from single view observations,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 3971–3980.
  124. D. Chen, Y. Liu, L. Huang, B. Wang, and P. Pan, “Geoaug: Data augmentation for few-shot nerf with geometry constraints,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XVII.   Springer, 2022, pp. 322–337.
  125. A. R. Kosiorek, H. Strathmann, D. Zoran, P. Moreno, R. Schneider, S. Mokrá, and D. J. Rezende, “Nerf-vae: A geometry aware 3d scene generative model,” in International Conference on Machine Learning.   PMLR, 2021, pp. 5742–5752.
  126. Y. Kim, S. Wiseman, A. Miller, D. Sontag, and A. Rush, “Semi-amortized variational autoencoders,” in International Conference on Machine Learning.   PMLR, 2018, pp. 2678–2687.
  127. J. Marino, Y. Yue, and S. Mandt, “Iterative amortized inference,” in International Conference on Machine Learning.   PMLR, 2018, pp. 3403–3412.
  128. V. Sitzmann, J. Martel, A. Bergman, D. Lindell, and G. Wetzstein, “Implicit neural representations with periodic activation functions,” Advances in Neural Information Processing Systems, vol. 33, pp. 7462–7473, 2020.
  129. Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao, “Ms-celeb-1m: A dataset and benchmark for large-scale face recognition,” in European conference on computer vision.   Springer, 2016, pp. 87–102.
  130. A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V. Koltun, “Carla: An open urban driving simulator,” in Conference on robot learning.   PMLR, 2017, pp. 1–16.
  131. W. Zhang, J. Sun, and X. Tang, “Cat head detection-how to effectively exploit shape and texture features,” in European conference on computer vision.   Springer, 2008, pp. 802–816.
  132. T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, pp. 4401–4410.
  133. T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 8110–8119.
  134. S. Cai, A. Obukhov, D. Dai, and L. Van Gool, “Pix2nerf: Unsupervised conditional p-gan for single image to neural radiance fields translation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 3981–3990.
  135. J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli, “Deep unsupervised learning using nonequilibrium thermodynamics,” in International Conference on Machine Learning.   PMLR, 2015, pp. 2256–2265.
  136. C. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. Denton, S. K. S. Ghasemipour, B. K. Ayan, S. S. Mahdavi, R. G. Lopes et al., “Photorealistic text-to-image diffusion models with deep language understanding,” arXiv preprint arXiv:2205.11487, 2022.
  137. R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 10 684–10 695.
  138. G. Metzer, E. Richardson, O. Patashnik, R. Giryes, and D. Cohen-Or, “Latent-nerf for shape-guided generation of 3d shapes and textures,” arXiv preprint arXiv:2211.07600, 2022.
  139. Y. Balaji, S. Nah, X. Huang, A. Vahdat, J. Song, K. Kreis, M. Aittala, T. Aila, S. Laine, B. Catanzaro et al., “ediffi: Text-to-image diffusion models with an ensemble of expert denoisers,” arXiv preprint arXiv:2211.01324, 2022.
  140. D. Xu, Y. Jiang, P. Wang, Z. Fan, Y. Wang, and Z. Wang, “Neurallift-360: Lifting an in-the-wild 2d photo to a 3d object with 360° views,” arXiv e-prints, pp. arXiv–2211, 2022.
  141. C. Deng, C. Jiang, C. R. Qi, X. Yan, Y. Zhou, L. Guibas, D. Anguelov et al., “Nerdi: Single-view nerf synthesis with language-guided diffusion as general image priors,” arXiv preprint arXiv:2212.03267, 2022.
  142. J. Gu, A. Trevithick, K.-E. Lin, J. Susskind, C. Theobalt, L. Liu, and R. Ramamoorthi, “Nerfdiff: Single-image view synthesis with nerf-guided distillation from 3d-aware diffusion,” arXiv preprint arXiv:2302.10109, 2023.
  143. J. Wynn and D. Turmukhambetov, “Diffusionerf: Regularizing neural radiance fields with denoising diffusion models,” arXiv preprint arXiv:2302.12231, 2023.
  144. R. Martin-Brualla, R. Pandey, S. Bouaziz, M. Brown, and D. B. Goldman, “Gelato: Generative latent textured objects,” in European Conference on Computer Vision.   Springer, 2020, pp. 242–258.
  145. A. Ahmadyan, L. Zhang, A. Ablavatski, J. Wei, and M. Grundmann, “Objectron: A large scale dataset of object-centric videos in the wild with pose annotations,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2021, pp. 7822–7831.
  146. L. Yen-Chen, P. Florence, J. T. Barron, A. Rodriguez, P. Isola, and T.-Y. Lin, “inerf: Inverting neural radiance fields for pose estimation,” in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2021, pp. 1323–1330.
  147. K. Jun-Seong, K. Yu-Ji, M. Ye-Bin, and T.-H. Oh, “Hdr-plenoxels: Self-calibrating high dynamic range radiance fields,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXII.   Springer, 2022, pp. 384–401.
  148. L. Li, Z. Shen, L. Shen, P. Tan et al., “Streaming radiance fields for 3d video synthesis,” in Advances in Neural Information Processing Systems, 2022.
  149. Q. Wang, Z. Wang, K. Genova, P. P. Srinivasan, H. Zhou, J. T. Barron, R. Martin-Brualla, N. Snavely, and T. Funkhouser, “Ibrnet: Learning multi-view image-based rendering,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 4690–4699.
  150. M. S. Sajjadi, H. Meyer, E. Pot, U. Bergmann, K. Greff, N. Radwan, S. Vora, M. Lučić, D. Duckworth, A. Dosovitskiy et al., “Scene representation transformer: Geometry-free novel view synthesis through set-latent scene representations,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 6229–6238.
  151. J. Reizenstein, R. Shapovalov, P. Henzler, L. Sbordone, P. Labatut, and D. Novotny, “Common objects in 3d: Large-scale learning and evaluation of real-life 3d category reconstruction,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 10 901–10 911.
  152. M. Adamkiewicz, T. Chen, A. Caccavale, R. Gardner, P. Culbertson, J. Bohg, and M. Schwager, “Vision-only robot navigation in a neural radiance world,” IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 4606–4613, 2022.
  153. J. Ichnowski, Y. Avigal, J. Kerr, and K. Goldberg, “Dex-nerf: Using a neural radiance field to grasp transparent objects,” in Conference on Robot Learning.   PMLR, 2022, pp. 526–536.
  154. J. Mahler, J. Liang, S. Niyaz, M. Laskey, R. Doan, X. Liu, J. A. Ojea, and K. Goldberg, “Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics,” arXiv preprint arXiv:1703.09312, 2017.
  155. J. Kerr, L. Fu, H. Huang, Y. Avigal, M. Tancik, J. Ichnowski, A. Kanazawa, and K. Goldberg, “Evo-nerf: Evolving nerf for sequential robot grasping of transparent objects,” in Conference on Robot Learning.   PMLR, 2022.
  156. M. Tancik, V. Casser, X. Yan, S. Pradhan, B. Mildenhall, P. P. Srinivasan, J. T. Barron, and H. Kretzschmar, “Block-nerf: Scalable large scene neural view synthesis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 8248–8258.
  157. H. Turki, D. Ramanan, and M. Satyanarayanan, “Mega-nerf: Scalable construction of large-scale nerfs for virtual fly-throughs,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12 922–12 931.
  158. Y. Xiangli, L. Xu, X. Pan, N. Zhao, A. Rao, C. Theobalt, B. Dai, and D. Lin, “Bungeenerf: Progressive neural radiance field for extreme multi-scale scene rendering,” in The European Conference on Computer Vision (ECCV), 2022.
  159. D. Derksen and D. Izzo, “Shadow neural radiance fields for multi-view satellite photogrammetry,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 1152–1161.
  160. W. Jang and L. Agapito, “Codenerf: Disentangled neural radiance fields for object categories,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 12 949–12 958.
  161. K. Kania, K. M. Yi, M. Kowalski, T. Trzciński, and A. Tagliasacchi, “Conerf: Controllable neural radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 18 623–18 632.
  162. X. Huang, Q. Zhang, Y. Feng, H. Li, X. Wang, and Q. Wang, “Hdr-nerf: High dynamic range neural radiance fields,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 18 398–18 408.
  163. L. Ma, X. Li, J. Liao, Q. Zhang, X. Wang, J. Wang, and P. V. Sander, “Deblur-nerf: Neural radiance fields from blurry images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12 861–12 870.
  164. N. Pearl, T. Treibitz, and S. Korman, “Nan: Noise-aware nerfs for burst-denoising,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12 672–12 681.
  165. C. Wang, X. Wu, Y.-C. Guo, S.-H. Zhang, Y.-W. Tai, and S.-M. Hu, “Nerf-sr: High quality neural radiance fields using supersampling,” in Proceedings of the 30th ACM International Conference on Multimedia, 2022, pp. 6445–6454.
  166. 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,” Advances in Neural Information Processing Systems, vol. 34, pp. 27 171–27 183, 2021.
  167. D. Azinović, R. Martin-Brualla, D. B. Goldman, M. Nießner, and J. Thies, “Neural rgb-d surface reconstruction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 6290–6301.
  168. Q. Fu, Q. Xu, Y.-S. Ong, and W. Tao, “Geo-neus: geometry-consistent neural implicit surfaces learning for multi-view reconstruction,” in Advances in Neural Information Processing Systems, 2022.
  169. Y. Wang, I. Skorokhodov, and P. Wonka, “Hf-neus: Improved surface reconstruction using high-frequency details,” in Advances in Neural Information Processing Systems, 2022.
  170. M. Oechsle, S. Peng, and A. Geiger, “Unisurf: Unifying neural implicit surfaces and radiance fields for multi-view reconstruction,” in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 5589–5599.
  171. S. Athar, Z. Xu, K. Sunkavalli, E. Shechtman, and Z. Shu, “Rignerf: Fully controllable neural 3d portraits,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 20 364–20 373.
  172. Y. Hong, B. Peng, H. Xiao, L. Liu, and J. Zhang, “Headnerf: A real-time nerf-based parametric head model,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 20 374–20 384.
  173. F. Zhao, W. Yang, J. Zhang, P. Lin, Y. Zhang, J. Yu, and L. Xu, “Humannerf: Efficiently generated human radiance field from sparse inputs,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 7743–7753.
  174. Z. Zheng, H. Huang, T. Yu, H. Zhang, Y. Guo, and Y. Liu, “Structured local radiance fields for human avatar modeling,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 15 893–15 903.
  175. R. Shao, H. Zhang, H. Zhang, M. Chen, Y.-P. Cao, T. Yu, and Y. Liu, “Doublefield: Bridging the neural surface and radiance fields for high-fidelity human reconstruction and rendering,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022, pp. 15 872–15 882.
  176. E. Corona, T. Hodan, M. Vo, F. Moreno-Noguer, C. Sweeney, R. Newcombe, and L. Ma, “Lisa: Learning implicit shape and appearance of hands,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 20 533–20 543.
  177. S. Peng, J. Dong, Q. Wang, S. Zhang, Q. Shuai, X. Zhou, and H. Bao, “Animatable neural radiance fields for modeling dynamic human bodies,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 2021, pp. 14 314–14 323.
  178. R. Li, J. Tanke, M. Vo, M. Zollhöfer, J. Gall, A. Kanazawa, and C. Lassner, “Tava: Template-free animatable volumetric actors,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXII.   Springer, 2022, pp. 419–436.
  179. G. Gafni, J. Thies, M. Zollhofer, and M. Nießner, “Dynamic neural radiance fields for monocular 4d facial avatar reconstruction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 8649–8658.
  180. J. Thies, M. Zollhofer, M. Stamminger, C. Theobalt, and M. Nießner, “Face2face: Real-time face capture and reenactment of rgb videos,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 2387–2395.
  181. M. Loper, N. Mahmood, J. Romero, G. Pons-Moll, and M. J. Black, “SMPL: A skinned multi-person linear model,” ACM Trans. Graphics (Proc. SIGGRAPH Asia), vol. 34, no. 6, pp. 248:1–248:16, Oct. 2015.
  182. S.-Y. Su, F. Yu, M. Zollhöfer, and H. Rhodin, “A-nerf: Articulated neural radiance fields for learning human shape, appearance, and pose,” Advances in Neural Information Processing Systems, vol. 34, pp. 12 278–12 291, 2021.
  183. L. Song, X. Gong, B. Planche, M. Zheng, D. Doermann, J. Yuan, T. Chen, and Z. Wu, “Pref: Predictability regularized neural motion fields,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXII.   Springer, 2022, pp. 664–681.
  184. X. Fu, S. Zhang, T. Chen, Y. Lu, L. Zhu, X. Zhou, A. Geiger, and Y. Liao, “Panoptic nerf: 3d-to-2d label transfer for panoptic urban scene segmentation,” arXiv preprint arXiv:2203.15224, 2022.
  185. S. Kobayashi, E. Matsumoto, and V. Sitzmann, “Decomposing nerf for editing via feature field distillation,” arXiv preprint arXiv:2205.15585, 2022.
  186. M. Zhang, S. Zheng, Z. Bao, M. Hebert, and Y.-X. Wang, “Beyond rgb: Scene-property synthesis with neural radiance fields,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023, pp. 795–805.
  187. Y. Yao, Z. Luo, S. Li, J. Zhang, Y. Ren, L. Zhou, T. Fang, and L. Quan, “Blendedmvs: A large-scale dataset for generalized multi-view stereo networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 1790–1799.
  188. L. Yariv, Y. Kasten, D. Moran, M. Galun, M. Atzmon, B. Ronen, and Y. Lipman, “Multiview neural surface reconstruction by disentangling geometry and appearance,” Advances in Neural Information Processing Systems, vol. 33, pp. 2492–2502, 2020.
  189. A. Elluswamy. Tesla, workshop on autonomous driving. CVPR 2022. [Online]. Available: https://www.youtube.com/watch?v=jPCV4GKX9Dw
  190. X. Long, C. Lin, P. Wang, T. Komura, and W. Wang, “Sparseneus: Fast generalizable neural surface reconstruction from sparse views,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXII.   Springer, 2022, pp. 210–227.
  191. N. Ruiz, Y. Li, V. Jampani, Y. Pritch, M. Rubinstein, and K. Aberman, “Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation,” arXiv preprint arXiv:2208.12242, 2022.
Citations (38)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets