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As a key technology in metaversa, wireless ultimate extended reality (XR) has attracted extensive attentions from both industry and academia.
2017
Earlier work this paper cites.
Y. Yang, J. Sun, H. Li, and Z. Xu, “ADMM-CSNet: A deep learning approach for image compressive sensing,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 42, no. 3, pp. 521–538, 2018
2018
Earlier work this paper cites.
A. Kanazawa, M. J. Black, D. W. Jacobs, and J. Malik, “End-to-end recovery of human shape and pose,” in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2018, pp. 7122–7131
2018
Earlier work this paper cites.
M. Jankowski, D. Gündüz, and K. Mikolajczyk, “Wireless image retrieval at the edge,” IEEE J. Sel. Areas Commun. , vol. 39, no. 1, pp. 89–100, 2020
2020
Earlier work this paper cites.
J. N. Martel, L. K. Mueller, S. J. Carey, P. Dudek, and G. Wetzstein, “Neural sensors: Learning pixel exposures for hdr imaging and video compressive sensing with programmable sensors,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 42, no. 7, pp. 1642–1653, 2020
2020
Earlier work this paper cites.
F. Mentzer, G. D. Toderici, M. Tschannen, and E. Agustsson, “High-fidelity generative image compression,” Advances Neural Info. Process. Systems (NIPS) , vol. 33, pp. 11 913–11 924, 2020
2020
Cited alongside, same era.
2021
Cited alongside, same era.
J. Li, H. Li, and Y. Matsushita, “Lighting, reflectance and geometry estimation from 360 panoramic stereo,” in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2021, pp. 10 586–10 595
2021
Cited alongside, same era.
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,” Commun. ACM , vol. 65, no. 1, pp. 99–106, 2021
2021
Cited alongside, same era.
I. F. Akyildiz and H. Guo, “Wireless extended reality (XR): Challenges and new research directions,” ITU J. Future Evol. Technol , vol. 3, 2022
2022
Closest in time.
D. G. Morín, P. Pérez, and A. G. Armada, “Toward the distributed implementation of immersive augmented reality architectures on 5G networks,” IEEE Commun. Mag. , vol. 60, no. 2, pp. 46–52, 2022
2022
Closest in time.
2022
Closest in time.
Y. Xu, S. Peng, C. Yang, Y. Shen, and B. Zhou, “3d-aware image synthesis via learning structural and textural representations,” in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. (CVPR) , 2022, pp. 18 430–18 439
2022
Closest in time.
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H. Xie, Z. Qin, G. Y. Li, and B.-H. Juang, “Deep learning enabled semantic communication systems,” IEEE Trans. Signal Process. , vol. 69, pp. 2663–2675, 2021
2021
Cited alongside, same era.
Y. Guo and Z. Qin, “Federated learning for multi-view synthesizing in wireless virtual reality networks,” in Proc. IEEE 96th Veh. Technol. Conf. (VTC Fall) , 2022, to appear
2022
Closest in time.