Skip to main content
Springer Nature Link
Log in

Complement decoded point cloud with coordinate adjustment for video-based point cloud compression

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Dynamic point cloud (DPC) represents a realistic 3D scene in motion and has a wide range of applications. Compressing point clouds has become crucial for storing and transmitting such data. Video-based point cloud compression (V-PCC) developed by the Moving Picture Expert Group can achieve remarkable performance in DPC compression. However, it also introduces issues of point reduction and coordinate distortion in the decoded DPC. In this paper, we present a 3D-based framework as a post-processing tool for the V-PCC decoder, which complements decoded DPC and performs coordinate adjustment. In particular, we propose a neighbor-based interpolation method to recover the missing points based on the coordinates in decoded DPC. Then, to minimize the coordinate distortion in interpolation, we design a sparse fully convolutional networks, 3D Minkowski Unet, to perform coordinate adjustment. Considering the variation of data size for DPC, we propose a cube-based patch generation method to enable the scalability of the proposed framework. The experiment results demonstrate that the proposed framework obtains significant performance in complementing reduced coordinates in both objective and subjective evaluation .

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Data Availability

No datasets were generated or analysed during the current study.

References

  1. Jang, E.S., Preda, M., Mammou, K., Tourapis, A.M., Kim, J., Graziosi, D.B., Rhyu, S., Budagavi, M.: 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 (2019)

    Article  Google Scholar 

  2. Cao, C., Preda, M., Zaharia, T.: 3d point cloud compression: a survey. In: The 24th international conference on 3D web technology, pp. 1–9. (2019)

  3. Liu, H., Yuan, H., Liu, Q., Hou, J., Liu, J.: A comprehensive study and comparison of core technologies for mpeg 3-d point cloud compression. IEEE Trans. Broadcast. 66(3), 701–717 (2019)

    Article  MATH  Google Scholar 

  4. Gumhold, S., Kami, Z., Isenburg, M., Seidel, H.-P.: Predictive point-cloud compression. In: ACM SIGGRAPH 2005 Sketches, pp. 137. (2005)

  5. Kammerl, J., Blodow, N., Rusu, R.B., Gedikli, S., Beetz, M., Steinbach, E.: Real-time compression of point cloud streams. In: 2012 IEEE international conference on robotics and automation, pp. 778–785 (2012). IEEE

  6. Guo, Y., Wang, H., Hu, Q., Liu, H., Liu, L., Bennamoun, M.: Deep learning for 3d point clouds: a survey. arxiv 2019. arXiv preprint arXiv:1912.12033

  7. Qi, C.R., Su, H., Mo, K., Guibas, L.J.: Pointnet: deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 652–660. (2017)

  8. Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Adv Neural Inf. Process. Syst. 30 (2017)

  9. Graham, B., Engelcke, M., Van Der Maaten, L.: 3d semantic segmentation with submanifold sparse convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 9224–9232. (2018)

  10. Choy, C., Gwak, J., Savarese, S.: 4d spatio-temporal convnets: Minkowski convolutional neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 3075–3084. (2019)

  11. Xing, J., Yuan, H., Hamzaoui, R., Liu, H., Hou, J.: Gqe-net: a graph-based quality enhancement network for point cloud color attribute. IEEE Trans. Image Process. 32, 6303–6317 (2023). https://doi.org/10.1109/TIP.2023.3330086

    Article  MATH  Google Scholar 

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

    Article  MATH  Google Scholar 

  13. Akhtar, A., Gao, W., Li, L., Li, Z., Jia, W., Liu, S.: Video-based point cloud compression artifact removal. IEEE Trans. Multimed. 24, 2866–2876 (2021)

    Article  MATH  Google Scholar 

  14. Liu, Y.-L., Chou, H.-S., Lee, M.-Z., Chan, M.-L., Lin, T.-L., Chen, C.-A., Chen, S.-L.: Point cloud inpainting based on delaunay triangulation. In: 2023 Asia Pacific signal and information processing association annual summit and conference (APSIPA ASC), pp. 1525–1529 (2023). IEEE

  15. Li, J., Zhou, J., Xiong, Y., Chen, X., Chakrabarti, C.: An adjustable farthest point sampling method for approximately-sorted point cloud data. In: 2022 IEEE workshop on signal processing systems (SiPS), pp. 1–6 (2022). IEEE

  16. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.: Inception-v4, inception-resnet and the impact of residual connections on learning. Proc. AAAI Conf. Artif Intell. (2017). https://doi.org/10.1609/aaai.v31i1.11231

  17. Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 834–848 (2017)

    Article  MATH  Google Scholar 

  18. Eon, B., Harrison, T., Myers, P., A., Chou: 8i Voxelized Full Bodies - A Voxelized Point Cloud Dataset,. MPEG/JPEG (2017)

  19. Schwarz, S., Martin-Cocher, G., Flynn, D., Budagavi, M.: Common test conditions for point cloud compression. Document ISO/IEC JTC1/SC29/WG11 w17766, Ljubljana, Slovenia (2018)

  20. Li, Z., Bao, J., Liu, Y., Au Yeung, S.-K., Zhu, S., Hung, K., Khan, M.A.: Sparse fully convolutional network for video-based point cloud compression color enhancement. In: 2023 6th artificial intelligence and cloud computing conference (AICCC), pp. 66–73. (2023)

Download references

Author information

Authors and Affiliations

  1. School of Science and Technology, Hong Kong Metropolitan University, Hong Kong, China

    Zeliang Li, Siu-Kei Au Yeung & Kevin Hung

  2. School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China

    Jingwei Bao, Yu Liu & Shuyuan Zhu

Authors
  1. Zeliang Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jingwei Bao
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Yu Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Siu-Kei Au Yeung
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Shuyuan Zhu
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Kevin Hung
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

All authors whose names appear on the submission made substantial contributions to the conception or design of the work; Z.L. wrote the main manuscript text and prepared all the materials for manuscript. All authors reviewed the manuscript and revised it critically for important intellectual content.

Corresponding author

Correspondence to Siu-Kei Au Yeung.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Z., Bao, J., Liu, Y. et al. Complement decoded point cloud with coordinate adjustment for video-based point cloud compression. SIViP 19, 48 (2025). https://doi.org/10.1007/s11760-024-03602-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11760-024-03602-6

Keywords