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Achieving 3D reconstruction from images captured under optimal conditions has been extensively studied in the vision and imaging fields.
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M. K. Kim, “Principles and techniques of digital holographic microscopy,” SPIE reviews 1
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B. Schwarz, “Mapping the world in 3d,” Nature Photonics 4
2010
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F. Yasuma, T. Mitsunaga, D. Iso, and S. K. Nayar, “Generalized assorted pixel camera: postcapture control of resolution, dynamic range, and spectrum,” IEEE transactions on image processing 19
2010
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2018
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2018
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P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas, “Learning representations and generative models for 3D point clouds,” in Proc. ICML, (2018), pp. 40–49
2018
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H. Rebecq, R. Ranftl, V. Koltun, and D. Scaramuzza, “Events-to-video: Bringing modern computer vision to event cameras,” in Proc. CVPR, (2019), pp. 3857–3866
2019
Cited alongside, same era.
2019
Cited alongside, same era.
V. Sitzmann, J. Thies, F. Heide, et al. , “DeepVoxels: Learning persistent 3D feature embeddings,” in Proc. CVPR, (2019), pp. 2437–2446
2019
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B. Mildenhall, P. P. Srinivasan, M. Tancik, et al. , “NeRF: representing scenes as neural radiance fields for view synthesis,” Communications of the ACM 65
2021
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H. Rebecq, R. Ranftl, V. Koltun, and D. Scaramuzza, “High speed and high dynamic range video with an event camera,” IEEE Transactions on Pattern Analysis and Machine Intelligence 43
V. Rudnev, M. Elgharib, C. Theobalt, and V. Golyanik, “EventNeRF: Neural radiance fields from a single colour event camera,” in Proc. CVPR, (2023), pp. 4992–5002
2023
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I. Hwang, J. Kim, and Y. M. Kim, “Ev-NeRF: Event based neural radiance field,” in Proc. WACV, (2023), pp. 837–847
2023
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Y. Qi, L. Zhu, Y. Zhang, and J. Li, “E 2 {}^{\mbox{2}} NeRF: Event enhanced neural radiance fields from blurry images,” in Proc. ICCV, (2023), pp. 13208–13218
2023
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T. Xiong et al. , “Event3DGS: Event-based 3D gaussian splatting for high-speed robot egomotion,” in Proc. CoRL, (2024)
2024
Closest in time.
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2021
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G. Gallego et al. , “Event-based vision: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence 44
2022
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J. Hidalgo-Carrió, G. Gallego, and D. Scaramuzza, “Event-aided direct sparse odometry,” in Proc. CVPR, (2022), pp. 5771–5780
2022
Cited alongside, same era.
S. Liu, T. Li, W. Chen, and H. Li, “A general differentiable mesh renderer for image-based 3D reasoning,” IEEE Transactions on Pattern Analysis and Machine Intelligence 44
2022
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S. Lin, Y. Ma, Z. Guo, and B. Wen, “Dvs-voltmeter: Stochastic process-based event simulator for dynamic vision sensors,” in ECCV, (2022)
2022
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2023
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B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis, “3D gaussian splatting for real-time radiance field rendering,” ACM Transactions on Graphics 42
2023
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S. Klenk, L. Koestler, D. Scaramuzza, and D. Cremers, “E-NeRF: Neural radiance fields from a moving event camera,” IEEE Robotics and Automation Letters 8
2023
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2024
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2024
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2024
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2024
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2024
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2024
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Y. Xiong et al. , “EfficientSAM: Leveraged masked image pretraining for efficient segment anything,” in Proc. CVPR, (2024), pp. 16111–16121
2024
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2025
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