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Compared with previous 3D reconstruction methods like Nerf, recent Generalizable 3D Gaussian Splatting (G-3DGS) methods demonstrate impressive efficiency even in the sparse-view setting.
Depth-Guided Robust and Fast Point Cloud Fusion NeRF for Sparse Input Views
Guo, S.; Wang, Q.; Gao, Y.; Xie, R.; and Song, L. 2024 · 1984
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A space-sweep approach to true multi-image matching
Collins, R. T. 1996 · 1996
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Image quality assessment: from error visibility to structural similarity
Wang, Z.; Bovik, A. C.; Sheikh, H. R.; and Simoncelli, E. P. 2004 · 2004
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Large scale multi-view stereopsis evaluation
Jensen, R.; Dahl, A.; Vogiatzis, G.; Tola, E.; and Aanæs, H. 2014 · 2014
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Adam: a method for stochastic optimization
Kingma, D. 2014 · 2014
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Squeeze-and-excitation networks
Hu, J.; Shen, L.; and Sun, G. 2018 · 2018
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Mvsnet: Depth inference for unstructured multi-view stereo
Yao, Y.; Luo, Z.; Li, S.; Fang, T.; and Quan, L. 2018 · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Zhang, R.; Isola, P.; Efros, A. A.; Shechtman, E.; and Wang, O. 2018 · 2018
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Stereo magnification: Learning view synthesis using multiplane images
Zhou, T.; Tucker, R.; Flynn, J.; Fyffe, G.; and Snavely, N. 2018 · 2018
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Cascade cost volume for high-resolution multi-view stereo and stereo matching
Gu, X.; Fan, Z.; Zhu, S.; Dai, Z.; Tan, F.; and Tan, P. 2020 · 2020
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Mip-nerf: A multiscale representation for anti-aliasing neural radiance fields
Barron, J. T.; Mildenhall, B.; Tancik, M.; Hedman, P.; Martin-Brualla, R.; and Srinivasan, P. P. 2021 · 2021
Earlier work this paper cites.
Nerf: Representing scenes as neural radiance fields for view synthesis
Mildenhall, B.; Srinivasan, P. P.; Tancik, M.; Barron, J. T.; Ramamoorthi, R.; and Ng, R. 2021 · 2021
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D-NeRF: Neural Radiance Fields for Dynamic Scenes
Pons-Moll, G.; Moreno-Noguer, F.; Corona, E.; and Pumarola, A. 2021 · 2021
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LoFTR: Detector-free local feature matching with transformers
Sun, J.; Shen, Z.; Wang, Y.; Bao, H.; and Zhou, X. 2021 · 2021
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pixelnerf: Neural radiance fields from one or few images
Yu, A.; Ye, V.; Tancik, M.; and Kanazawa, A. 2021 · 2021
Cited alongside, same era.
Depth-supervised nerf: Fewer views and faster training for free
Deng, K.; Liu, A.; Zhu, J.-Y.; and Ramanan, D. 2022 · 2022
Cited alongside, same era.
Transmvsnet: Global context-aware multi-view stereo network with transformers
Ding, Y.; Yuan, W.; Zhu, Q.; Zhang, H.; Liu, X.; Wang, Y.; and Liu, X. 2022 · 2022
Cited alongside, same era.
Regnerf: Regularizing neural radiance fields for view synthesis from sparse inputs
Niemeyer, M.; Barron, J. T.; Mildenhall, B.; Sajjadi, M. S.; Geiger, A.; and Radwan, N. 2022 · 2022
Cited alongside, same era.
Rethinking depth estimation for multi-view stereo: A unified representation
Peng, R.; Wang, R.; Wang, Z.; Lai, Y.; and Wang, R. 2022 · 2022
Cited alongside, same era.
High-resolution image synthesis with latent diffusion models
Unifying flow, stereo and depth estimation
Xu, H.; Zhang, J.; Cai, J.; Rezatofighi, H.; Yu, F.; Tao, D.; and Geiger, A. 2023 · 2023
Later among the works it cites.
pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction
Charatan, D.; Li, S. L.; Tagliasacchi, A.; and Sitzmann, V. 2024 · 2024
Closest in time.
Mvsplat: Efficient 3d gaussian splatting from sparse multi-view images
Chen, Y.; Xu, H.; Zheng, C.; Zhuang, B.; Pollefeys, M.; Geiger, A.; Cham, T.-J.; and Cai, J. 2024 · 2024
Closest in time.
Depth-regularized optimization for 3d gaussian splatting in few-shot images
Chung, J.; Oh, J.; and Lee, K. M. 2024 · 2024
Closest in time.
Instantsplat: Unbounded sparse-view pose-free gaussian splatting in 40 seconds
Fan, Z.; Cong, W.; Wen, K.; Wang, K.; Zhang, J.; Ding, X.; Xu, D.; Ivanovic, B.; Pavone, M.; Pavlakos, G.; et al. 2024 · 2024
Closest in time.
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Rombach, R.; Blattmann, A.; Lorenz, D.; Esser, P.; and Ommer, B. 2022 · 2022
Cited alongside, same era.
DiffusionDepth: Diffusion Denoising Approach for Monocular Depth Estimation
Duan, Y.; Guo, X.; and Zhu, Z. 2023 · 2023
Cited alongside, same era.
Neo 360: Neural fields for sparse view synthesis of outdoor scenes
Irshad, M. Z.; Zakharov, S.; Liu, K.; Guizilini, V.; Kollar, T.; Gaidon, A.; Kira, Z.; and Ambrus, R. 2023 · 2023
Cited alongside, same era.
3D Gaussian Splatting for Real-Time Radiance Field Rendering
Kerbl, B.; Kopanas, G.; Leimkühler, T.; and Drettakis, G. 2023 · 2023
Cited alongside, same era.
Li, Z.; Zhang, C.; Ma, W.-C.; Zhou, Y.; Huang, L.; Wang, H.; Lim, S.; and Zhao, H. 2023 · 2023
Cited alongside, same era.
Animatable 3D Gaussian: Fast and High-Quality Reconstruction of Multiple Human Avatars
Liu, Y.; Huang, X.; Qin, M.; Lin, Q.; and Wang, H. 2023 · 2023
Cited alongside, same era.
Gluestick: Robust image matching by sticking points and lines together
Pautrat, R.; Suárez, I.; Yu, Y.; Pollefeys, M.; and Larsson, V. 2023 · 2023
Cited alongside, same era.
A General Implicit Framework for Fast NeRF Composition and Rendering
Gao, X.; Yang, Z.; Zhao, Y.; Sun, Y.; Jin, X.; and Zou, C. 2024 · 2024
Closest in time.
Dngaussian: Optimizing sparse-view 3d gaussian radiance fields with global-local depth normalization
Li, J.; Zhang, J.; Bai, X.; Zheng, J.; Ning, X.; Zhou, J.; and Gu, L. 2024 · 2024
Closest in time.
Scaffold-gs: Structured 3d gaussians for view-adaptive rendering
Lu, T.; Yu, M.; Xu, L.; Xiangli, Y.; Wang, L.; Lin, D.; and Dai, B. 2024 · 2024
Closest in time.
ColNeRF: Collaboration for Generalizable Sparse Input Neural Radiance Field
Ni, Z.; Yang, P.; Yang, W.; Wang, H.; Ma, L.; and Kwong, S. 2024 · 2024
Closest in time.
ZeroRF: Fast Sparse View 360deg Reconstruction with Zero Pretraining
Shi, R.; Wei, X.; Wang, C.; and Su, H. 2024 · 2024
Closest in time.
latentsplat: Autoencoding variational gaussians for fast generalizable 3d reconstruction
Wewer, C.; Raj, K.; Ilg, E.; Schiele, B.; and Lenssen, J. E. 2024 · 2024
Closest in time.
Yang, L.; Kang, B.; Huang, Z.; Zhao, Z.; Xu, X.; Feng, J.; and Zhao, H. 2024 · 2024
Closest in time.
M2Depth: Self-supervised Two-Frame Multi-camera Metric Depth Estimation
Zou, Y.; Ding, Y.; Qiu, X.; Wang, H.; and Zhang, H. 2024 · 2024
Closest in time.