Е-Публикации
Технически университет - София

Abstract: Holographic-Type Communication promises real-time transmission of holographic content, ensuring fully immersive experiences for remote users. However, it requires accurate capture, efficient compression, and low-latency transmission of voluminous three-dimensional data. This paper specifically focuses on the three-dimensional point cloud compression of large indoor environment scenes, using deep autoencoder architecture. Additionally, the paper proposes an algorithm for point cloud augmentation to enhance model compression efficiency.

References

1. "Draco 3d Graphics Compression, " https://google.github.io/draco/
2. R. B. Rusu and S. Cousins, "3d is here: Point cloud library (pcl), " in 2011 IEEE international conference on robotics and automation. IEEE, 2011, pp. 1-4.S.
3. S. Schwarz, M. Preda, V. Baroncini, M. Budagavi, P. Cesar, P. A. Chou, R. A. Cohen, M. Krivokuca, S. Lasserre, Z. Li et al., "Emerging mpeg standards for point cloud compression, " IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 9, no. 1, 2018.
4. 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-pcc) and geometry-based (gpcc), " APSIPA Transactions on Signal and Information Processing, 2020.
5. C. R. Qi, H. Su, K. Mo, and L. J. Guibas, "Pointnet: Deep learning on point sets for 3d classification and segmentation, " in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017.
6. C. R. Qi, L. Yi, H. Su, and L. J. Guibas, "Pointnet++: Deep hierarchical feature learning on point sets in a metric space, " Advances in neural information processing systems, vol. 30, 2017.
7. P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas, "Learning representations and generative models for 3d point clouds, " in International conference on machine learning. PMLR, 2018, pp. 40-49.
8. W. Yan, S. Liu, T. H. Li, Z. Li, G. Li et al., "Deep autoencoder-based lossy geometry compression for point clouds, " arXiv preprint arXiv:1905.03691, 2019.
9. T. Huang and Y. Liu, "3d point cloud geometry compression on deep learning, " in Proceedings of the 27th ACM international conference on multimedia, 2019, pp. 890-898.
10. L. Wiesmann, A. Milioto, X. Chen, C. Stachniss, and J. Behley, "Deep compression for dense point cloud maps, " IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2060-2067, 2021.
11. Y. He, X. Ren, D. Tang, Y. Zhang, X. Xue, and Y. Fu, "Densitypreserving deep point cloud compression, " in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 2333-2342.
12. A. Chang, A. Dai, T. Funkhouser, M. Halber, M. Niessner, M. Savva, S. Song, A. Zeng, and Y. Zhang, "Matterport3d: Learning from rgb-d data in indoor environments, " International Conference on 3D Vision, 2017.
13. G. Baruch, Z. Chen, A. Dehghan, T. Dimry, Y. Feigin, P. Fu, T. Gebauer, B. Joffe, D. Kurz, A. Schwartz, and E. Shulman, "ARKitscenes-A diverse real-world dataset for 3d indoor scene understanding using mobile RGB-d data, " in 35th Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2021.
14. I. Armeni, A. Sax, A. R. Zamir, and S. Savarese, "Joint 2D-3DSemantic Data for Indoor Scene Understanding, " ArXiv e-prints, 2017.
15. B.S. Hua, Q.-H. Pham, D. T. Nguyen, M.-K. Tran, L.F. Yu, and S.K. Yeung, "Scenenn: A scene meshes dataset with annotations, " in International Conference on 3D Vision (3DV), 2016.
16. Q. Zhu, L. Fan, and N. Weng, "Advancements in point cloud data augmentation for deep learning: A survey, " Pattern Recognition, p. 110532, 2024

Issue

Copyright IEEE