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A novel compression framework of the dense point-cloud model for cultural heritage artifacts

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Abstract

With the help of laser scanner, the accurate digital information of cultural relics can be obtained. However, how to transfer the enormous and dense data by an efficient way, is still the key problem for computer-aided cultural relic protection. In this paper, we proposed a novel framework for compression and reconstruction of the dense point cloud model for cultural heritage artifacts. Firstly, the collected point cloud model were regarded as 3D geometric signals, and an octree method based on a hash function is utilized to divide the neighborhood relationship. Then, the discrete Laplacian sparse basis of 3D geometric signals is constructed, and the sensing matrix is further obtained by the stochastic Gauss matrix. However, the sensing matrix is always enormous, which means that in practice, it will cause a huge amount of computation and slow recovery. To solve this problem, we proposed a Truncated Singular Value Decomposition (TSVD)-based low rank approximation approach for the inverse reconstruction. Further, a preconditioning method is investigated to reduce the coherence of the converted sensing matrix. In order to test the performance of our framework, the 3D point cloud model of terracotta warriors and tri-coloured glazed pottery of Hu people are adopted. Experimental results demonstrate that our method can well recover the weak texture cultural heritage artifacts of the 3D point cloud model. The results achieved here are significant for virtual display and data processing of the dense point cloud model.

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References

  1. Shou-Lin LU (2012) China's Exploration for the Concepts of Cultural Relics Conservation[J]. South Cult 1(1):10–22

    Google Scholar 

  2. Zhang Y, Li K, Chen X, Zhang S, Geng G (2018) A multi feature fusion method for reassembly of 3D cultural heritage artifacts[J]. J Cult Herit 33:191–200

    Article  Google Scholar 

  3. Grosman L, Smikt O, Smilansky U (2008) On the application of 3-D scanning technology for the documentation and typology of lithic artifacts[J]. J Archaeol Sci

  4. Zhou P, Shui W, Qu L, et al. (2016) Case study: missing data computation and 3D printing application in symmetrical artifact restoration[C]// the symposium. ACM

  5. Peng X, Huang W, Wen P, Wu X (2009) Simplification of scattered point cloud based on feature extraction. 2009 Third International Conference on Genetic and Evolutionary Computing, Guilin, pp. 335–338

  6. Xiao Z, Huang W (2009) Kd-tree Based Nonuniform Simplification of 3D Point Cloud. 2009 Third international conference on genetic and evolutionary computing, Guilin, pp. 339–342. https://doi.org/10.1109/WGEC.2009

  7. Zhao-Wen Q, Tian-Wen Z (2008) Key techniques on cultural relic 3D reconstruction[J]. Acta Electron Sin 36(12):2423

    Google Scholar 

  8. Taubin G, Rossignac J (1998) Geometric compression through topological surgery[J]. ACM Trans Graph 3D Cult Relic 17(2):84–115

    Article  Google Scholar 

  9. Taubin G (2001) 3D geometry compression recent advances and challenges. Proceedings Ninth Pacific Conference on Computer Graphics and Applications. Pacific Graphics 2001, Tokyo, Japan, pp 2-2

  10. Taubin G (2003) New results in signal processing and compression of polygon meshes. 2003 Shape Modeling International., Seoul, South Korea, pp. 45

  11. An X, Yu X., Zhang Y (2015) Research on the self-similarity of point cloud outline for accurate compression. 2015 International Conference on Smart and Sustainable City and Big Data (ICSSC), Shanghai, pp. 170–174

  12. Shuyu P, Ning D, Wang L, Chunkang Z (2018) Compression algorithm of point cloud data based on adaptive layering [J]. Appl Res Comput 35(11):3500–3503 + 3507

    Google Scholar 

  13. Jayant N (1992) Signal compression: technology targets and research directions[J]. IEEE J Sel Areas Commun 10(5):796–818

    Article  Google Scholar 

  14. Du Z-M, Geng G-H (2011) 3-D geometric signal compression method based on compressed sensing. 2011 InternationalConference on image analysis and signal processing, Hubei, pp. 62–66

  15. Xiao S , Lv Z , Zhou X (2015) A lung 3D model reconstruction method based on compressed sensing and MRI[C]// 2015 IET international conference on biomedical image and signal processing (ICBISP 2015). IET

  16. Zhu S, Zhu C (2019) A new image compression-encryption scheme based on compressive sensing and cyclic shift[J]. Multimed Tools Appl 78:20855–20875

    Article  Google Scholar 

  17. Zhai X, Cheng Z, Wei Y et al (2019) Opt Eng 58(1):1

    Article  Google Scholar 

  18. Meenu R, Dhok SB, R. B. (2018) Deshmukh. A Systematic Review of Compressive Sensing: Concepts, Implementations and Applications[J]. IEEE Access PP(99):1–1

    Google Scholar 

  19. Zhang F, Fan H, Liu P, Li J (2020) Image Denoising using hybrid singular value thresholding operators. IEEE Access 8:8157–8165

    Article  Google Scholar 

  20. Gao P, Rong J, Pu H, Liu T, Zhang W, Zhang X, Lu H (2018) Sparse view cone beam X-ray luminescence tomography based on truncated singular value decomposition. Opt Express 26(18):23233–23250

    Article  Google Scholar 

  21. Yang B, Dong Z (2013) A shape-based segmentation method for mobile laser scanning point clouds[J]. ISPRS J Photogramm Remote Sens 81(Complete):19–30

    Article  Google Scholar 

  22. Cheng-Lei Y, Zong-Xia Z, Rong-Jiang P et al (2006) Research on system framework and some key Technologies for Computer Aided Cultural Relics Reconstruction[J]. J Syst Simul 18(7):2003–1998

    Google Scholar 

  23. Zheng S-y, Zhou Y, Huang R-y (2014) A method of 3D measurement and reconstruction for cultural relics in museums[J]. Sci Surv Mapp XXXIX-B5:145–149

    Google Scholar 

  24. Wu LS, Shi HL, Chen HW (2016) Denoising of three-dimensional point data based on classification of feature information[J]. Guangxue Jingmi Gongcheng/Opt Precis Eng 24(6):1465–1473

    Google Scholar 

  25. Cohen RA, Tian D, Vetro A (2016) [IEEE 2016 Data Compression Conference (DCC) - Snowbird, UT, USA (2016.3.30–2016.4.1)] 2016 Data Compression Conference (DCC) - Point Cloud Attribute Compression Using 3-D Intra Prediction and Shape-Adaptive Transforms[C]// Data Compression Conference. IEEE Computer Society, pp.141–150

  26. Celik S, Basaran M, Erkucuk S, et al. (2016) Comparison of compressed sensing based algorithms for sparse signal reconstruction[C]// 2016 24th signal processing and communication application conference (SIU). IEEE

  27. Tsaig Y, Donoho DL (2006) Extensions of compressed sensing.[J]. Signal Process 86(3):549–571

    Article  Google Scholar 

  28. Schnabel R, Klein R (2006) Octree-based point-cloud compression[C]// symposium on point based graphics, Boston, Massachusetts, USA, 2006. Proceedings. Eurographics Association

  29. Sorkine O, Cohen-Or D. (2004) Least-squares meshes[C]// shape modeling applications. IEEE

  30. Candès EJ, Romberg JK, Tao T (2006) Stable Signal Recovery from Incomplete and Inaccurate Measurements[J]. Commun Pure Appl Math 59(8):1207–1223

    Article  MathSciNet  Google Scholar 

  31. Edelman A (1997) The Probability that a Random Real Gaussian Matrix haskReal Eigenvalues, Related Distributions, and the Circular Law[J]. J Multivar Anal 60(2):203–232

    Article  Google Scholar 

  32. Pejakovic T, Orovic M, Orovic I (2015) A comparison of CS reconstruction algorithms for multicomponent nonlinear phase signals[C]// 2015 4th Mediterranean conference on embedded computing (MECO). IEEE

  33. Lee S, Nedic A (2012) Distributed random projection algorithm for convex optimization[J]. IEEE J Sel Top Signal Process 7(2):221–229

    Article  Google Scholar 

  34. Solimene R (2012) A novel CS-TSVD strategy to perform data reduction in linear inverse scattering problems[J]. IEEE Geosci Remote Sens Lett 9(5):881–885

    Article  Google Scholar 

  35. Jin A, Yazici B, Ale A, Ntziachristos V (2012) Preconditioning of the fluorescence diffuse optical tomography sensing matrix based on compressive sensing[J]. Opt Lett 37(20):4326–4328

    Article  Google Scholar 

  36. Golub GH, von Matt U (1997) Generalized cross-validation for large scale problems[C]//

  37. Hansen PC (1992) Analysis of Discrete Ill-Posed Problems by Means of the L-Curve[J]. SIAM Rev 34(4):561–580

    Article  MathSciNet  Google Scholar 

  38. Chan TF, Hansen PC (1990) Computing Truncated Singular Value Decomposition Least Squares Solutions by Rank Revealing QR-Factorizations[J]. SIAM J Sci Stat Comput 11(3):519–530

    Article  MathSciNet  Google Scholar 

  39. Caikou C, Yu H (2012) A matching pursuit based similarity measure for face recognition Proceedings of the 31st Chinese Control Conference, Hefei, pp. 3886–3890

  40. Liu G, DeBrunner V (2010) Matching Pursuits may yield superior results to Orthogonal Matching Pursuits when secondary information is estimated from the signal model. 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers, Pacific Grove, CA, pp 2013-2016

  41. Kim S, Koh K, Lustig M, Boyd S, Gorinevsky D (2007) An interior-point method for large-scale ‘1-regularized leastsquares. IEEE J Sel Top Sig Proc 1(4):606–617

    Article  Google Scholar 

  42. Figueiredo MAT, Nowak RD, Wright SJ (2007) Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE J Sel Top Signal Process 1(4):586–597

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank all the reviewers for their valuable comments. This research was funded by the National Key Research and Development Program of China (No. 2019YFC1521102; No.2019YFC1521103), the Key Research and Development Program of Shaanxi Province of China (No. 2019GY-215), the National Natural Science Foundation of China (No.61902317; No.61772421; No.2017YFB1402103; No.61731015); the Science and Technology Plan Program in Shaanxi Province of China (No.2019JQ-166); the Major Industrial Chain Projects in ShaanXi Province of China (No. 2019ZDLSF07-02).

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Author notes

  1. Haibo Zhang and Kang Li contributed equally to this work.

Authors and Affiliations

  1. School of Information Sciences and Technology, Northwest University, Xi’an, China

    Haibo Zhang, Kang Li, Jiaojiao Kou, Linqi Hai, Junbo Zhang, Mingquan Zhou, Guohua Geng & Shunli Zhang

  2. Tencent Technology, Chendu, Sichuan, China

    Xiaoxue Chen

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  1. Haibo Zhang
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Correspondence to Shunli Zhang.

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Zhang, H., Li, K., Kou, J. et al. A novel compression framework of the dense point-cloud model for cultural heritage artifacts. Multimed Tools Appl 81, 32817–32839 (2022). https://doi.org/10.1007/s11042-022-13084-8

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