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A Sampling-based 3D Point Cloud Compression Algorithm for Immersive Communication

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Abstract

3D point cloud is one of the most common and basic 3D object representation model that is widely used in virtual/augmented reality applications, e.g., immersive communication. Compression of 3D point cloud is a big challenge because of its huge data volume and irregular data structure. In this paper, we propose a sampling-based compression algorithm for 3D point clouds. First, a 3D point cloud was resampled by a graph filter to obtain a subset of representative 3D points. Then, the representative points were compressed by the G-PCC (geometry-based point cloud compression) encoder software that was released by MPEG. Finally, the decoded representative points were used to reconstruct the original 3D point clouds by a CNN-based up-sampling approach. Experimental results demonstrate that a significant (73.15%) bit rate reduction can be achieved by the proposed 3D point cloud compression algorithm with minimal quality degradation of the reconstructed 3D point clouds.

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References

  1. Schnabel R, Klein R (2006) Octree-based point-cloud compression. In: Proceedings of the Eurographics Symposium on Point-Based Graphics, pp 111–121

  2. Huang Y, Peng J, Kuo J, Gopi M (2008) A generic scheme for progressive point cloud coding. In: Proceedings of the IEEE Transactions on Visualization and Computer Graphics, vol 14, no 2, pp 440–453

  3. Zhang C, Florêncio D., Loop C (2014) Point cloud attribute compression with graph transform. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), Paris, pp 2066–2070

  4. Xu Y, Zhu W, Xu Y, Li Z (2019) Dynamic Point Cloud Geometry Compression via Patch-wise Polynomial Fitting. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP, Brighton United Kingdom, pp 2287–2291

  5. Gumhold S, Kami Z, Isenburg M, Hans-Peter S (2005) Predictive point-cloud compression. In: J Buhler (ed) ACM SIGGRAPH Sketches SIGGRAPH ’05. ACM, New York, Article 137

  6. Morell V, Orts S, Cazorla M (2014) Geometric 3d point cloud compression. Pattern Recognition Letters 50:55–62

    Article  Google Scholar 

  7. Yan W, Shao Y, Liu S, H Li T, Li Z, Li G (2019) Deep AutoEncoder-based Lossy Geometry Compression for Point Clouds. arXiv:1905.03691v1

  8. Yang Y, Feng C, Shen Y, Tian D (2018) foldingnet: Point Cloud Auto-Encoder via Deep Grid Deformation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, pp 206–215

  9. Tredinnick R, Broecker M, Ponto K (2016) Progressive feedback point cloud rendering for virtual reality display. In: Proceedings of the IEEE Virtual Reality VR, Greenville SC, pp 301–302

  10. Xin W, Pu J (2010) An Improved ICP Algorithm for Point Cloud Registration. In: Proceedings of the International Conference on Computational and Information Sciences Chengdu 565– 568

  11. Kamencay P, Sinko M, Hudec R, Benco M, Radil R (2019) Improved Feature Point Algorithm for 3D Point Cloud Registration. In: Proceedings of the 42nd International Conference on Telecommunications and Signal Processing TSP, Budapest Hungary, pp 517–520

  12. Gojcic Z, Zhou C, Wegner JD, Wieserl A (2019) The Perfect Match: 3D Point Cloud Matching with Smoothed Densities. arXiv:1811.06879v2

  13. Aoki Y, Goforth H, Srivatsan RA, Lucey S (2019) PointNetLK: Robust & Efficient Point Cloud Registration using PointNet. arXiv:1903.05711

  14. Li M, Yin D (2017) A fast segmentation method of sparse point clouds. In: Proceedings of the 29th Chinese Control And Decision Conference CCDC, Chongqing, pp 3561–3565

  15. Hongbo J, Qisong J (2017) TLS point cloud segmentation based on points features. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium IGARSS Fort Worth TX, pp 1720–1723

  16. Charle QR, Su H, Kaichun M, Guibas LJ (2017) pointnet: Deep Learning on Point Sets for 3D Classification and Segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition CVPR Honolulu HI 77–85

  17. Qi CR, Yi L, Su H, Guibas LJ (2017) PointNet++: Deep hierarchical feature learning on point sets in a metric space. In: Proceedings of the Advances in Neural Information Processing Systems NIPS, pp 5099–5108

  18. Tchapmi L, Choy C, Armeni I, Gwak J, Savarese S (2017) SEGC loud: Semantic Segmentation of 3D Point Clouds. In: Proceedings of the International Conference on 3D Vision 3 DV Qingdao, pp 537–547

  19. Roveri R, Rahmann L, Öztireli AC, Gross M (2018) A network architecture for point cloud classification via automatic depth images generation. In: Proceedings of the IEEE / CVF conference on computer vision and pattern recognition, Salt Lake City UT, pp 4176–4184

  20. Chen H, Huang TS (1985) octrees: Construction, Representation, And Manipulation. In: Proceedings of the SPIE Proceedings 0579 Intelligent Robots and Computer Vision IV Dec

  21. Duan C, Chen S, Kovacevic J (2019) 3D point cloud denoising via deep neural network based local surface estimation. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing ICASSP, Brighton United Kingdom, pp 8553–8557. https://doi.org/10.1109/ICASSP.2019.8682812

  22. Yu L, Li X, Fu C, Cohen-Or D, Heng P (2018) PU-net: Point Cloud Upsampling Network. In: Proceedings of the IEEE/ CVF Conference on Computer Vision and Pattern recognition Salt Lake City UT, pp 2790–2799

  23. Wang Y, Wu S, Huang H, Cohen-Or D, Sorkine-Hornung O (2019) Patch-based Progressive 3D Point Set Upsampling. arXiv:1811.11286

  24. Bossen F (2012) Common Test Conditions and Software Reference Configurations. In: Proceedings of the 9th Meeting of the JCT-VC in Geneva

  25. Cui L, Xu H (2017) Hybrid color attribute compression for point cloud data. In: Proceedings of the IEEE International Conference on Multimedia and Expo ICME Hong Kong, pp 1273–1278

  26. Zhang K, Zhu W, Xu Y, Liu N (2018) Point cloud attribute compression via clustering and intra prediction. In: Proceedings of the IEEE international symposium on broadband multimedia systems and broadcasting BMSB Valencia, pp 1–5

  27. He L, Zhu W, Xu Y (2017) Best-effort projection based attribute compression for 3D point cloud. In: Proceedings of the 23rd Asia-Pacific Conference on Communications APCC Perth WA, pp 1–6

  28. Cohen RA, Tian D, Vetro A (2016) Attribute compression for sparse point clouds using graph transforms. In: Proceedings of the IEEE International Conference on Image Processing ICIP Phoenix AZ, pp 1374–1378

  29. Shao Y, Zhang Z, Li Z, Fan K, Li G (2017) Attribute compression of 3D point clouds using Laplacian sparsity optimized graph transform. In: Proceedings of the IEEE Visual Communications and Image Processing VCIP, St. Petersburg, FL, pp 1–4

  30. Zhang X, Wu X, Wu F (2007) Image Coding on Quincunx Lattice with Adaptive Lifting and Interpolation. In: Proceedings of the Data Compression Conference DCC ’07, Snowbird UT, pp 193–202

  31. Zhang X, Wu X (2008) Can Lower Resolution Be Better? In: Proceedings of the Data Compression Conference dcc 2008, Snowbird UT, pp 302–311

  32. Jiaji WU, Xing Y, Shi G, Jiao L (2009) Image compression with downsampling and overlapped transform at low bit rates. In: Proceedings of the 16th IEEE International Conference on Image Processing ICIP Cairo, pp 29–32

  33. Akbari A, Trocan M (2018) Downsampling Based Image Coding Using Dual Dictionary Learning and Sparse Representations. In: IEEE 20th International Workshop on Multimedia Signal Processing MMSP Vancouver BC, pp 1–5

  34. Zhang Y, Zhao D, Zhang J, Xiong R, Gao W (2011) Interpolation-Dependent Image downsampling. IEEE Trans Image Process 20(11):3291–3296

    Article  MathSciNet  Google Scholar 

  35. Wang Y, Wang L, Wang H, Li P (2018) Information-Compensated Downsampling for image Super-Resolution. IEEE Signal Processing Letters 25(5):685–689

    Article  Google Scholar 

  36. An X, Yu X, Xu Q, Wang J (2014) Research on 3D scanning point cloud de-nosing. In: Proceedings of the International Conference on Audio, Language and Image Processing Shanghai, pp 842–845

  37. Qi J, Hu W, Guo Z (2019) Feature Preserving and Uniformity-Controllable Point Cloud Simplification on Graph. In: Proceedings of the IEEE International Conference on Multimedia and Expo ICME Shanghai China, pp 284–289

  38. Chen S, Tian D, Feng C, Vetro A, Kovačević J. (2018) Fast resampling of Three-Dimensional point clouds via graphs. In: IEEE Transactions on Signal Processing, vol 66, no 3, pp 666–681

  39. Chen S, Tian D, Feng C, Vetro A, Kovačević J (2017) Contour-enhanced resampling of 3D point clouds via graphs. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing ICASSP, New Orleans LA, pp 2941–2945

  40. Hou HS (1978) Cubic splines for image interpolation and digital filtering. In: IEEE Trans. Acoustic, Speech, Signal Process., vol ASSP-26, no 6, pp 508–517

  41. Keys RG (1981) Cubic convolution interpolation for digital image processing. In: IEEE Trans. Acoust., Speech, Signal Process., vol ASSP-29, pp 1153–1160

  42. Ledig C et al (2017) Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp 105–114

  43. Shi W et al (2016) Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition CVPR Las Vegas NV, pp 1874–1883

  44. Alexa M, Behr J, Cohen-Or D, Fleishman S, Levin D, Silva CT (2003) Computing and rendering point set surfaces. In: IEEE Transactions on Visualization and Computer Graphics, vol 9, no 1, pp 3–15

  45. Huang H, Li D, Zhang H, Ascher U, Cohen-Or D (2009) Consolidation of unorganized point clouds for surface reconstruction. ACM Trans on Graphics SIGGRAPH Asia 28(5:176):1–8

    Google Scholar 

  46. Test Model v5 G -P C C (2019) ISO/IEC JTC 1/ SC 29 WG 11 Doc. n18174, marrakech MAR

  47. – “G-PCC codec description ” (2019) ISO/IEC JTC1/SC29/WG11, 125th meeting, Marrakech, Tech. Rep. w18189

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Acknowledgements

This work was supported in part by Shenzhen Science and Technology Research and Development Funds under Grant JCYJ20170818103244664; in part by the National Natural Science Foundation of China under Grants 61571274 and 61871342; in part by the National Key R&D Program of China under Grants 2018YFC0831003; in part by the Shandong Provincial Key Research and Development Plan under Grant 2017CXGC1504; in part by the open project program of state key laboratory of virtual reality technology and systems, Beihang University, under Grant VRLAB2019B03; and in part by the Young Scholars Program of Shandong University (YSPSDU) under Grant 2015WLJH39.

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Authors and Affiliations

  1. Shenzhen Research Institute of Shandong University, Shenzhen, Guangdong Province, China, and the School of Control Science and Engineering, Ji’nan, Shandong Province, China

    Hui Yuan

  2. College of Information Science and Engineering, Qilu Normal University, No. 2 Wenbo Road, Zhangqiu District, Ji’nan, Shangdong Province, China

    Dexiang Zhang

  3. School of Information Science and Engineering, Shandong University, Qingdao, Shandong Province, China

    Weiwei Wang & Yujun Li

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  1. Hui Yuan
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  2. Dexiang Zhang
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  3. Weiwei Wang
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  4. Yujun Li
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Correspondence to Yujun Li.

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Yuan, H., Zhang, D., Wang, W. et al. A Sampling-based 3D Point Cloud Compression Algorithm for Immersive Communication. Mobile Netw Appl 25, 1863–1872 (2020). https://doi.org/10.1007/s11036-020-01570-y

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