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The field of 3D reconstruction from images has rapidly evolved in the past few years, first with the introduction of Neural Radiance Field (NeRF) and more recently with 3D Gaussian Splatting (3DGS).
Pérez, P., Gangnet, M., Blake, A.: Poisson image editing. In: ACM SIGGRAPH 2003 Papers. p. 313–318. SIGGRAPH ’03, Association for Computing Machinery, New York, NY, USA (2003). https://doi.org/10.1145/1201775.882269, https://doi.org/10.1145/1201775.882269
2003
Earlier work this paper cites.
Kingma, D., Ba, J.: Adam: A method for stochastic optimization. In: International Conference on Learning Representations (ICLR) (2015)
2015
Earlier work this paper cites.
Liu, R., Lehman, J., Molino, P., Petroski Such, F., Frank, E., Sergeev, A., Yosinski, J.: An intriguing failing of convolutional neural networks and the coordconv solution. In: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 31. Curran Associates, Inc. (2018), https://proceedings.neurips.cc/paper_files/paper/2018/file/60106888f8977b71e1f15db7bc9a88d1-Paper.pdf
2018
Earlier work this paper cites.
Liu, R., Lehman, J., Molino, P., Petroski Such, F., Frank, E., Sergeev, A., Yosinski, J.: An intriguing failing of convolutional neural networks and the coordconv solution. Advances in neural information processing systems 31
2018
Earlier work this paper cites.
Yao, Y., Luo, Z., Li, S., Fang, T., Quan, L.: Mvsnet: Depth inference for unstructured multi-view stereo. European Conference on Computer Vision (ECCV) (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Yifan, W., Serena, F., Wu, S., Öztireli, C., Sorkine-Hornung, O.: Differentiable surface splatting for point-based geometry processing. ACM Transactions on Graphics (TOG) 38
2019
Earlier work this paper cites.
Bemana, M., Myszkowski, K., Seidel, H.P., Ritschel, T.: X-fields: Implicit neural view-, light- and time-image interpolation. ACM Transactions on Graphics (Proc. SIGGRAPH Asia 2020) 39
2020
Earlier work this paper cites.
Luo, X., Huang, J., Szeliski, R., Matzen, K., Kopf, J.: Consistent video depth estimation. ACM Transactions on Graphics (Proceedings of ACM SIGGRAPH) 39
2020
Earlier work this paper cites.
Mildenhall, B., Srinivasan, P.P., Tancik, M., Barron, J.T., Ramamoorthi, R., Ng, R.: Nerf: Representing scenes as neural radiance fields for view synthesis. In: ECCV (2020)
2020
Earlier work this paper cites.
Pumarola, A., Corona, E., Pons-Moll, G., Moreno-Noguer, F.: D-NeRF: Neural Radiance Fields for Dynamic Scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
2020
Earlier work this paper cites.
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 (ICCV). pp. 5855–5864 (October 2021)
2021
Earlier work this paper cites.
Chan, E., Monteiro, M., Kellnhofer, P., Wu, J., Wetzstein, G.: pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis. In: Proc. CVPR (2021)
2021
Earlier work this paper cites.
Chen, A., Xu, Z., Zhao, F., Zhang, X., Xiang, F., Yu, J., Su, H.: Mvsnerf: Fast generalizable radiance field reconstruction from multi-view stereo. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 14124–14133 (2021)
2021
Earlier work this paper cites.
Chibane, J., Bansal, A., Lazova, V., Pons-Moll, G.: Stereo radiance fields (srf): Learning view synthesis from sparse views of novel scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (jun 2021)
2021
Earlier work this paper cites.
Du, Y., Zhang, Y., Yu, H.X., Tenenbaum, J.B., Wu, J.: Neural radiance flow for 4d view synthesis and video processing. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2021)
2021
Earlier work this paper cites.
Gao, C., Saraf, A., Kopf, J., Huang, J.B.: Dynamic view synthesis from dynamic monocular video. In: Proceedings of the IEEE International Conference on Computer Vision (2021)
2021
Earlier work this paper cites.
Garbin, S.J., Kowalski, M., Johnson, M., Shotton, J., Valentin, J.: Fastnerf: High-fidelity neural rendering at 200fps. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 14346–14355 (October 2021)
2021
Earlier work this paper cites.
Jain, A., Tancik, M., Abbeel, P.: Putting nerf on a diet: Semantically consistent few-shot view synthesis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 5885–5894 (October 2021)
2021
Earlier work this paper cites.
Kopf, J., Rong, X., Huang, J.B.: Robust consistent video depth estimation. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2021)
2021
Earlier work this paper cites.
Li, Z., Niklaus, S., Snavely, N., Wang, O.: Neural scene flow fields for space-time view synthesis of dynamic scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2021)
2021
Earlier work this paper cites.
Lin, C.H., Ma, W.C., Torralba, A., Lucey, S.: Barf: Bundle-adjusting neural radiance fields. In: IEEE International Conference on Computer Vision (ICCV) (2021)
2021
Earlier work this paper cites.
Reiser, C., Peng, S., Liao, Y., Geiger, A.: Kilonerf: Speeding up neural radiance fields with thousands of tiny mlps. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 14335–14345 (October 2021)
2021
Earlier work this paper cites.
Tretschk, E., Tewari, A., Golyanik, V., Zollhöfer, M., Lassner, C., Theobalt, C.: Non-rigid neural radiance fields: Reconstruction and novel view synthesis of a dynamic scene from monocular video. In: IEEE International Conference on Computer Vision (ICCV). IEEE (2021)
2021
Cited alongside, same era.
Wang, Q., Wang, Z., Genova, K., Srinivasan, P., Zhou, H., Barron, J.T., Martin-Brualla, R., Snavely, N., Funkhouser, T.: Ibrnet: Learning multi-view image-based rendering. In: CVPR (2021)
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Yen-Chen, L., Florence, P., Barron, J.T., Rodriguez, A., Isola, P., Lin, T.Y.: iNeRF: Inverting neural radiance fields for pose estimation. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2021)
2021
Xia, Y., Tang, H., Timofte, R., Gool, L.V.: Sinerf: Sinusoidal neural radiance fields for joint pose estimation and scene reconstruction. In: 33rd British Machine Vision Conference 2022, BMVC 2022, London, UK, November 21-24, 2022. BMVA Press (2022), https://bmvc2022.mpi-inf.mpg.de/0131.pdf
2022
Later among the works it cites.
Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Zip-nerf: Anti-aliased grid-based neural radiance fields. ICCV (2023)
2023
Later among the works it cites.
Bian, W., Wang, Z., Li, K., Bian, J., Prisacariu, V.A.: Nope-nerf: Optimising neural radiance field with no pose prior. In: CVPR (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
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Cited alongside, same era.
Yu, A., Ye, V., Tancik, M., Kanazawa, A.: pixelNeRF: Neural radiance fields from one or few images. In: CVPR (2021)
2021
Cited alongside, same era.
Attal, B., Huang, J.B., Zollhöfer, M., Kopf, J., Kim, C.: Learning neural light fields with ray-space embedding networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
2022
Cited alongside, same era.
Barron, J.T., Mildenhall, B., Verbin, D., Srinivasan, P.P., Hedman, P.: Mip-nerf 360: Unbounded anti-aliased neural radiance fields. CVPR (2022)
2022
Cited alongside, same era.
Chen, A., Xu, Z., Geiger, A., Yu, J., Su, H.: Tensorf: Tensorial radiance fields. In: European Conference on Computer Vision (ECCV) (2022)
2022
Cited alongside, same era.
Chng, S.F., Ramasinghe, S., Sherrah, J., Lucey, S.: Gaussian activated neural radiance fields for high fidelity reconstruction and pose estimation. In: The European Conference on Computer Vision: ECCV (2022)
2022
Cited alongside, same era.
Deng, K., Liu, A., Zhu, J.Y., Ramanan, D.: Depth-supervised NeRF: Fewer views and faster training for free. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2022)
2022
Cited alongside, same era.
Gu, J., Liu, L., Wang, P., Theobalt, C.: Stylenerf: A style-based 3d aware generator for high-resolution image synthesis. In: International Conference on Learning Representations (2022), https://openreview.net/forum?id=iUuzzTMUw9K
2022
Cited alongside, same era.
Guo, Y.C., Kang, D., Bao, L., He, Y., Zhang, S.H.: Nerfren: Neural radiance fields with reflections. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 18409–18418 (June 2022)
2022
Cited alongside, same era.
Guangcong, Chen, Z., Loy, C.C., Liu, Z.: Sparsenerf: Distilling depth ranking for few-shot novel view synthesis. IEEE/CVF International Conference on Computer Vision (ICCV) (2023)
2023
Later among the works it cites.
Höllein, L., Cao, A., Owens, A., Johnson, J., Nießner, M.: Text2room: Extracting textured 3d meshes from 2d text-to-image models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV). pp. 7909–7920 (October 2023)
2023
Later among the works it cites.
Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics 42
2023
Later among the works it cites.
N, N.S., Soundararajan, R.: Vip-nerf: Visibility prior for sparse input neural radiance fields. ACM SIGGRAPH 2023 Conference Proceedings (2023), https://api.semanticscholar.org/CorpusID:258426778
2023
Later among the works it cites.
Paliwal, A., Tsarov, A., Kalantari, N.K.: Implicit view-time interpolation of stereo videos using multi-plane disparities and non-uniform coordinates. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2023)
2023
Later among the works it cites.
Seo, S., Chang, Y., Kwak, N.: Flipnerf: Flipped reflection rays for few-shot novel view synthesis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 22883–22893 (2023)
2023
Later among the works it cites.
Shi, X., Huang, Z., Li, D., Zhang, M., Cheung, K.C., See, S., Qin, H., Dai, J., Li, H.: Flowformer++: Masked cost volume autoencoding for pretraining optical flow estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1599–1610 (2023)
2023
Later among the works it cites.
T, M.V., Wang, P., Chen, X., Chen, T., Venugopalan, S., Wang, Z.: Is attention all that neRF needs? In: The Eleventh International Conference on Learning Representations (2023), https://openreview.net/forum?id=xE-LtsE-xx
2023
Later among the works it cites.
Wang, G., Chen, Z., Loy, C.C., Liu, Z.: Sparsenerf: Distilling depth ranking for few-shot novel view synthesis. In: IEEE/CVF International Conference on Computer Vision (ICCV) (2023)
2023
Later among the works it cites.
Wang, P., Liu, Y., Chen, Z., Liu, L., Liu, Z., Komura, T., Theobalt, C., Wang, W.: F2-nerf: Fast neural radiance field training with free camera trajectories. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 4150–4159 (June 2023)
2023
Later among the works it cites.
Xiong, H., Muttukuru, S., Upadhyay, R., Chari, P., Kadambi, A.: Sparsegs: Real-time 360° sparse view synthesis using gaussian splatting. Arxiv (2023)
2023
Later among the works it cites.
Xiong, K., Peng, R., Zhang, Z., Feng, T., Jiao, J., Gao, F., Wang, R.: Cl-mvsnet: Unsupervised multi-view stereo with dual-level contrastive learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3769–3780 (2023)
2023
Later among the works it cites.
Yang, J., Pavone, M., Wang, Y.: Freenerf: Improving few-shot neural rendering with free frequency regularization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8254–8263 (2023)
2023
Later among the works it cites.
Zeng, L., Kalantari, N.K.: Test-time optimization for video depth estimation using pseudo reference depth. Computer Graphics Forum (2023). https://doi.org/https://doi.org/10.1111/cgf.14729, https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14729
2023
Later among the works it cites.
Zhang, Z., Peng, R., Hu, Y., Wang, R.: Geomvsnet: Learning multi-view stereo with geometry perception. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 21508–21518 (2023)
2023
Later among the works it cites.
Zhu, Z., Fan, Z., Jiang, Y., Wang, Z.: Fsgs: Real-time few-shot view synthesis using gaussian splatting (2023)
2023
Later among the works it cites.
Li, J., Zhang, J., Bai, X., Zheng, J., Ning, X., Zhou, J., Gu, L.: Dngaussian: Optimizing sparse-view 3d gaussian radiance fields with global-local depth normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 20775–20785 (2024)
2024
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Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., Zhao, H.: Depth anything: Unleashing the power of large-scale unlabeled data. In: CVPR (2024)
2024
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