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Improved grid refine segmentation for 3D point cloud in video-based point cloud compression (V-PCC)

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Abstract

For an immersive visual communication experience, it is essential to enable technologies that can capture and transmit point clouds capable of accurately depicting a photorealistic impression. The MPEG standardization group has devised a method for compressing video-based point clouds called Video-based Point Cloud Compression (V-PCC). In the V-PCC process, it first projects a 3D point cloud onto 2D planes called 3D patch generation, consisting of normal estimation, initial segmentation, grid refine segmentation (GRS), and patch segmentation. Afterwards, the High-Efficiency Video Coding (HEVC) standard is utilized for 2D video compression. However, the GRS is the most time-consuming process in 3D patch generation; a state-of-the-art method has evolved to address this issue. In this paper, we propose an improved approach that eliminates redundant execution steps and speeds up the optimization process of GRS by predicting the convergence of the determined point cloud projection plane during iterations. Our experimental results show that our approach is more persuasive, reducing execution time by up to 15% with slight increases or decreases in the BD rate compared to the state-of-the-art method.

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References

  1. Hu Q, Yang B, Xie L, Rosa S, Guo Y, Wang Z, Trigoni N, Markham A (2021) Learning semantic segmentation of large-scale point clouds with random sampling. IEEE Trans Pattern Anal Mach Intell 44(11):8338–8354

    Google Scholar 

  2. Fuchs H, State A, Bazin J-C (2014) Immersive 3d telepresence. Computer 47(7):46–52

    Article  Google Scholar 

  3. Zhu L, Xiang Y, Song A (2022) Visible Patches for Haptic Rendering of Point Clouds. IEEE Trans Haptics 15(3):497–507

    Article  Google Scholar 

  4. Jang ES, Preda M, Mammou K, A. M. tourapis, J. Kim, D. B. Graziosi, S. Rhyu, and M. Budagavi (2019) Video-based point-cloud-compression standard in MPEG: from evidence collection to committee draft [standards in a nutshell]. IEEE Signal Process Mag 36(3):118–123. https://doi.org/10.1109/MSP.2019.2900721

    Article  Google Scholar 

  5. Schwarz S, Preda M, Baroncini V, Budagavi M, Cesar P, Chou PA, Cohen RA, Krivokuća M, Lasserre S, Li Z, Llach J, Mammou K, Mekuria R, Nakagami O, Siahaan E, Tabatabai A, Tourapis AM, Zakharchenko V (2019) Emerging MPEG standards for point cloud compression. IEEE J Emerg Select Top Circ Syst 9(1):133–148

    Article  Google Scholar 

  6. Cui L, Mekuria R, Preda M, Jang E (2019) Point-cloud compression: moving picture experts group’s new standard in 2020. IEEE Consumer Electron Mag 8(4):17–21

    Article  Google Scholar 

  7. ISO/IEC FDIS 23090–5 Information technology — Coded representation of immersive media — Part 5: Visual Volumetric Video-based Voding (V3C) and Video-based Point Cloud Compression (V-PCC). Available: https://www.iso.org/standard/73025.html. Accessed 2021

  8. Li L, Li Z, Zakharchenko V, Chen J, Li H (2020) Advanced 3D motion prediction for video-based dynamic point cloud compression. IEEE Trans Image Process 29:289–302

    Article  MathSciNet  Google Scholar 

  9. Li L, Li Z, Liu S, Li H (2020) Rate control for video-based point cloud compression. IEEE Trans Image Process 29:6237–6250

    Article  MathSciNet  Google Scholar 

  10. Li L, Li Z, Liu S, Li H (2021) Occupancy-map-based rate distortion optimization and partition for video-based point cloud compression, IEEE Trans Circ Syst Vid Technol 31(1):326–338. https://doi.org/10.1109/TCSVT.2020.2966118

  11. Li L, Li Z, Liu S, Li H (2020) Efficient projected frame padding for video-based point cloud compression, IEEE Trans Multimed https://doi.org/10.1109/TMM.2020.3016894

  12. Liu Q, Yuan H, Hou J, Hamzaoui R, Su H (2020) Model-based joint bit allocation between geometry and colour for video-based 3D point cloud compression, IEEE Trans Multimed, vol. 23 https://doi.org/10.1109/TMM.2020.3023294

  13. Kim J, Im J, Rhyu S, Kim K (2020) 3D Motion estimation and compensation method for video based-point cloud compression, IEEE Access https://doi.org/10.1109/ACCESS.2020.2991478

  14. Rhyu S, Kim J, Im J, Kim K (2020) Contextual homogeneity-based patch decomposition method for higher point cloud compression. IEEE Access 8:207805–207812

    Article  Google Scholar 

  15. Cao K, Cosman P (2021) Denoising and inpainting for point clouds compressed by V-PCC. IEEE Access 9:107688–107700

    Article  Google Scholar 

  16. Dong T, Kim K, Jang ES (2021) Performance evaluation of the codec agnostic approach in MPEG-I video-basd point cloud compression, IEEE Access. https://doi.org/10.1109/ACCESS.2021.3137036

  17. Lin T-L, Bu H-B, Chen Y-C, Yang J-R, Liang C-F, Jiang K-H, Lin C-H, Yue X-F (2021) Efficient quadtree search for HEVC coding units for V-PCC. IEEE Access 9:139109–139121

    Article  Google Scholar 

  18. Kim J, Kim Y-H (2021) Fast grid-based refining segmentation method in video-based point cloud compression, IEEE Access, vol. 9. https://doi.org/10.1109/ACCESS.2021.3084180

  19. Schnabel R, Klein R (2006) Octree-based point-cloud compression. In: Botsch M, Chen B, Pauly M, Zwicker M (eds) Symposium on Point-Based Graphics, The Eurographics Association. https://doi.org/10.2312/SPBG/SPBG06/111-120

  20. Lasserre S, Flynn D, Qu S (2019) Using neighbouring nodes for the compression of octrees representing the geometry of point clouds, in Proceedings of the 10th ACM Multimedia Systems Conference (MMSys '19). Association for Computing Machinery, New York, NY, USA,145–153. https://doi.org/10.1145/3304109.3306224

  21. Kammerl J, Blodow N, Rusu RB, Gedikli S, Beetz M, Steinbach E, “Real-time compression of point cloud streams, in, (2012) IEEE international conference on robotics and automation. IEEE 2012:778–785

  22. de Oliveira Rente P, Brites C, Ascenso J, Pereira F (2018) Graph-based static 3d point clouds geometry coding. IEEE Trans Multimed 21(2):284–299

    Article  Google Scholar 

  23. de Queiroz RL, Garcia DC, Chou PA, Florencio DA (2018) Distance-based probability model for octree coding. IEEE Signal Process Lett 25(6):739–742

    Article  Google Scholar 

  24. Garcia DC, Fonseca TA, Ferreira RU, de Queiroz RL (2019) Geometry coding for dynamic voxelized point clouds using octrees and multiple contexts. IEEE Trans Image Process 29:313–322

    Article  MathSciNet  Google Scholar 

  25. Zhang X, Gao W, Liu S, “Implicit geometry partition for point cloud compression”, in (2020) Data Compression Conference (DCC). IEEE 2020:73–82

  26. Ochotta T, Saupe D (2004) Compression of point-based 3D models by shape-adaptive wavelet coding of multi-height fields. In: Proceedings of the first Eurographics conference conference on Point-Based Graphics (SPBG'04). Eurographics Association, Goslar, DEU, 103–112.

  27. Morell V, Orts S, Cazorla M, Garcia-Rodriguez J (2014) Geometric 3d point cloud compression. Pattern Recogn Lett 50:55–62

    Article  Google Scholar 

  28. Tu C, Takeuchi E, Miyajima C, Takeda K, “Compressing continuous point cloud data using image compression methods”, in (2016) IEEE 19th international conference on intelligent transportation systems (ITSC). IEEE 2016:1712–2171

  29. 3DG "Common Test Conditions for point cloud compression" (2018) ISO/IEC JTC1/SC29/WG11, N18883. Gothernburg

  30. Meynet G, Nehmé Y, Digne J, Lavoué G (2020) Pcqm: A full-reference quality metric for colored 3d oint clouds, in 2020 Twelfth International Conference on Quality of Multimedia Experience (QoMEX). IEEE, pp. 1–6. https://doi.org/10.1109/QoMEX48832.2020.9123147

  31. Lavoue G (2011) A Multiscale Metric for 3D Mesh Visual Quality Assessment. Comput Graph Forum 30(5):1427–1437

    Article  Google Scholar 

  32. Lissner J, Preiss P, Urban MS, Lichtenauer and P. Zolliker (2013) Image-difference prediction: From grayscale to color. IEEE Trans Image Process 22(2):435–446

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work was supported by the Ministry of Science and Technology, Taiwan, under grant numbers MOST-110-2221-E-027-044-MY3, MOST 111-2221-E-033-041 and NSTC 112-2221-E-033-049-MY3

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Authors and Affiliations

  1. Department of Electronic Engineering, National Taipei University of Technology, Taipei City, 10608, Taiwan

    Ting-Lan Lin

  2. Department of Electronic Engineering, Chung Yuan Christian University, Taoyuan City, 320314, Taiwan

    Ching-Hsuan Lin, Yih-Shyh Chiou & Shih-Lun Chen

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  1. Ting-Lan Lin
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Lin, TL., Lin, CH., Chiou, YS. et al. Improved grid refine segmentation for 3D point cloud in video-based point cloud compression (V-PCC). Multimed Tools Appl 83, 62701–62720 (2024). https://doi.org/10.1007/s11042-023-17845-x

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