SISIM: statistical information similarity-based point cloud quality assessment
Author:
Funder
National Natural Science Foundation of China
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s00371-024-03352-z.pdf
Reference42 articles.
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2. Pereira, F.: Deep learning-based point cloud coding for immersive experiences. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 7368–7370 (2022). https://doi.org/10.1145/3503161.3546961
3. Jia, X., Yang, S., Wang, Y., et al.: Dual-view 3D reconstruction via learning correspondence and dependency of point cloud regions. IEEE Trans. Image Process. 31, 6831–6846 (2022). https://doi.org/10.1109/TIP.2022.3215024
4. Chen, J., Zhang, Y., Huang, K., et al.: Self-supervised boundary point prediction task for point cloud domain adaptation. IEEE Robot. Autom. Lett. 8, 5878–5885 (2023). https://doi.org/10.1109/LRA.2023.3301278
5. Yuan, H., Zhang, D., Wang, W., et al.: A sampling-based 3D point cloud compression algorithm for immersive communication. Mob. Netw. Appl. 25, 1863–1872 (2020). https://doi.org/10.1007/s11036-020-01570-y
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