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This paper presents novel solutions for the efficient and reliable transmission of point clouds over wireless channels for real-time applications.
Y. Polyanskiy, H. V. Poor, and S. Verdu, “Channel coding rate in the finite blocklength regime,” IEEE Trans. Info. Theory , vol. 56, no. 5, pp. 2307–2359, 2010
2010
Earlier work this paper cites.
R. B. Rusu and S. Cousins, “3 D
2011
Earlier work this paper cites.
2015
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3 D
2017
Earlier work this paper cites.
H. Thomas, C. R. Qi, J.-E. Deschaud, B. Marcotegui, F. Goulette, and L. J. Guibas, “Kpconv: Flexible and deformable convolution for point clouds,” in CVPR , 2019, pp. 6411–6420
2019
Earlier work this paper cites.
E. Bourtsoulatze, D. B. Kurka, and D. Gündüz, “Deep joint source-channel coding for wireless image transmission,” IEEE Trans. Cogn. Commun. Netw. , vol. 5, no. 3, pp. 567–579, 2019
2019
Earlier work this paper cites.
L. Huang, S. Wang, K. Wong, J. Liu, and R. Urtasun, “Oct S
2020
Earlier work this paper cites.
S. Wiedemann, H. Kirchhoffer, S. Matlage, P. Haase, A. Marban, T. Marinč, D. Neumann, T. Nguyen, H. Schwarz, T. Wiegand, D. Marpe, and W. Samek, “Deepcabac: A universal compression algorithm for deep neural networks,” IEEE J. Sel. Topics Signal Process. , vol. 14, no. 4, pp. 700–714, 2020
2020
Earlier work this paper cites.
D. Graziosi, O. Nakagami, S. Kuma, A. Zaghetto, T. Suzuki, and A. Tabatabai, “An overview of ongoing point cloud compression standardization activities: Video-based ( V
2020
Earlier work this paper cites.
“Common test conditions for point cloud compression,” ISO/IEC JTC1/SC29/WG11 MPEG output document N19084 , Feb. 2020
2020
Earlier work this paper cites.
H. Zhao, L. Jiang, J. Jia, P. H. Torr, and V. Koltun, “Point transformer,” in ICCV , 2021, pp. 16 259–16 268
2021
Earlier work this paper cites.
M.-H. Guo, J.-X. Cai, Z.-N. Liu, T.-J. Mu, R. R. Martin, and S.-M. Hu, “ Pct
2021
Earlier work this paper cites.
J. Wang, D. Ding, Z. Li, and Z. Ma, “Multiscale point cloud geometry compression,” in IEEE Data Compression Conf. (DCC) , 2021, pp. 73–82
2021
Earlier work this paper cites.
L. Wiesmann, A. Milioto, X. Chen, C. Stachniss, and J. Behley, “Deep compression for dense point cloud maps,” IEEE Robot. Autom. Lett. , vol. 6, no. 2, pp. 2060–2067, 2021
2021
Cited alongside, same era.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “ NeRF
2021
Cited alongside, same era.
2021
Cited alongside, same era.
T. Fujihashi, T. Koike-Akino, S. Chen, and T. Watanabe, “Wireless 3d point cloud delivery using deep graph neural networks,” in ICC 2021 - IEEE Int. Conf. Commun. , 2021, pp. 1–6
2021
Cited alongside, same era.
J. Xu, B. Ai, W. Chen, A. Yang, P. Sun, and M. Rodrigues, “Wireless image transmission using deep source channel coding with attention modules,” IEEE Trans. Circuits Syst. Video Technol. , 2021
T. Fujihashi, T. Koike-Akino, T. Watanabe, and P. V. Orlik, “Holocast+: Hybrid digital-analog transmission for graceful point cloud delivery with graph F
2022
Later among the works it cites.
2022
Later among the works it cites.
C. Bian, Y. Shao, and D. Gündüz, “Wireless point cloud transmission,” in IEEE Int’l Wrksp. Signal Proc. Advances in Wireless Comms. (SPAWC) , 2024
2024
Closest in time.
2024
Closest in time.
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2021
Cited alongside, same era.
C. Park, Y. Jeong, M. Cho, and J. Park, “Fast point transformer,” in CVPR , 2022, pp. 16 949–16 958
2022
Cited alongside, same era.
J. Zhang, G. Liu, D. Ding, and Z. Ma, “Transformer and upsampling-based point cloud compression,” in Proc. 1st Int. Workshop Adv. Point Cloud Compr., Process. Anal. , 2022, pp. 33–39
2022
Cited alongside, same era.
Y. Hu and Y. Wang, “Learning neural volumetric field for point cloud geometry compression,” in Picture Coding Symposium (PCS) , 2022, pp. 127–131
2022
Cited alongside, same era.
2022
Cited alongside, same era.
D. Gündüz, Z. Qin, I. E. Aguerri, H. S. Dhillon, Z. Yang, A. Yener, K. K. Wong, and C.-B. Chae, “Beyond transmitting bits: Context, semantics, and task-oriented communications,” IEEE J. Sel. Area Commun. , 2022
2022
Cited alongside, same era.
M. Yang, C. Bian, and H.-S. Kim, “ OFDM
2022
Cited alongside, same era.
J. Dai, S. Wang, K. Tan, Z. Si, X. Qin, K. Niu, and P. Zhang, “Nonlinear transform source-channel coding for semantic communications,” IEEE J. Sel. Area Commun. , vol. 40, no. 8, pp. 2300–2316, 2022
2022
Cited alongside, same era.
2024
Closest in time.
H. Wu, Y. Shao, C. Bian, K. Mikolajczyk, and D. Gündüz, “Deep joint source-channel coding for adaptive image transmission over MIMO
2024
Closest in time.
M. Bokaei, J. Jensen, S. Doclo, and J. Østergaard, “Low-latency deep analog speech transmission using joint source channel coding,” IEEE J. Sel. Topics Signal Process. , vol. 18, no. 8, pp. 1401–1413, 2024
2024
Closest in time.
M. Jankowski, D. Gündüz, and K. Mikolajczyk, “ AirNet
2024
Closest in time.
Y. Huang, B. Bai, Y. Zhu, X. Qiao, X. Su, L. Yang, and P. Zhang, “ ISCom
2024
Closest in time.
T. Fujihashi, S. Kato, and T. Koike-Akino, “Implicit neural representation for low-overhead graph-based holographic-type communications,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP) , 2024, pp. 2825–2829
2024
Closest in time.
2024
Closest in time.
C. Bian, Y. Shao, H. Wu, E. Ozfatura, and D. Gündüz, “Process-and-forward: Deep joint source-channel coding over cooperative relay networks,” IEEE J. Sel. Area Commun. , 2025
2025
Closest in time.