Skip to main content
SpringerLink
Log in

A fast reconstruction method of the dense point-cloud model for cultural heritage artifacts based on compressed sensing and sparse auto-encoder

  • Published:
Optical and Quantum Electronics Aims and scope Submit manuscript

Abstract

Since the point cloud data of cultural heritage artifacts obtained by the laser scanner is enormous and dense, these lead to a large quantity of network resource for storing, processing, and transmission. This paper provided a fast reconstruction method of the dense point cloud model for cultural relics based on sparse auto-encoder and compressed sensing. Firstly, the octree method based on the hash function was utilized to extract local features and remove redundant points. Secondly, the point cloud, which can be seen as the 3D geometric signal, is projected to the discrete Laplacian sparse basis via the point cloud adjacency matrix. Then, aiming at the bottleneck of slow recovery caused by tremendous scale of inverse problem based on compressed sensing theory, the sparse auto-encoder was applied to reduce the dimension and speed up the recovery. Finally, the OMP algorithm was applied to reconstruct 3D point cloud model based on the stochastic Gauss matrix. In order to test the performance of our methods, the 3D point cloud model of terracotta warriors and horses head were used. And the experimental results demonstrated that our approach can obviously accelerate the process of reconstruction of the dense point cloud model for the cultural heritage artifacts and ensure the recovery accuracy.

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

Access this article

Log in via an institution

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

Similar content being viewed by others

References

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

  • Cao, L.J., Chua, K.S., Chong, W.K., et al.: A comparison of PCA, KPCA and ICA for dimensionality reduction in support vector machine. Neurocomputing 55(1–2), 321–336 (2003)

    Google Scholar 

  • Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)?—Arguments against avoiding RMSE in the literature, Geosci. Model Dev. 7, 1247–1250 (2014)

    Article  Google Scholar 

  • Chen, S., Zhao, H., Kong, M., et al.: 2D-LPP: a two-dimensional extension of locality preserving projections. Neurocomputing 70(4–6), 912–921 (2007)

    Article  Google Scholar 

  • Cohen, R.A., Tian, D., Vetro, A.: Point Cloud Attribute Compression Using 3-D Intra Prediction and Shape-Adaptive Transforms. Data Compression Conference (DCC), Snowbird, UT, pp. 141–150 (2016a)

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

  • De Queiroz, R.L., Chou, P.A.: Compression of 3D point clouds using a region-adaptive hierarchical transform.  IEEE Trans Image Process 25(8), 3947–3956 (2016)

    Article  ADS  MathSciNet  Google Scholar 

  • Du, Z.-M., Geng, G.-H.: 3-D geometric signal compression method based on compressed sensing. In: 2011 International Conference on Image Analysis and Signal Processing, Hubei, pp. 62–66 (2011)

  • Fan, B., Rao, Y., Wei, L., et al.: Region-based growing algorithm for 3D reconstruction from MRI images. In: 2017 2nd International Conference on Image, Vision and Computing (ICIVC), Chengdu, pp. 521–525 (2017)

  • Hao, W., Han, M., Hao, W.: Compressed sensing remote sensing image reconstruction based on wavelet tree and nonlocal total variation. In: 2016 International Conference on Network and Information Systems for Computers (ICNISC), Wuhan, pp. 317–322 (2016)

  • Hoegg, T., Lefloch, D., Kolb, A.: Time-of-Flight camera based 3D point cloud reconstruction of a car. Comput. Ind. 64(9), 1099–1114 (2013)

    Article  Google Scholar 

  • Iscen, A., Avrithis, Y., Tolias, G., et al.: Fast spectral ranking for similarity search. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, pp. 7632–7641 (2018)

  • Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. IEEE (2004)

  • Kortelainen, J., Vayrynen, E., Seppanen, T.: Isomap approach to EEG-based assessment of neurophysiological changes during anesthesia. IEEE Trans. Neural Syst. Rehabil. Eng. 19(2), 113–120 (2011)

    Article  Google Scholar 

  • Le, Q.V., Ranzato, M., Monga, R., et al.: Building high-level features using large scale unsupervised learning. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, Vancouver, BC, pp. 8595–8598 (2013)

  • Levey, A., Lindenbaum, M.: Sequential Karhunen–Loeve basis extraction and its application to images. IEEE Trans. Image Process. 9(8), 1371–1374 (2000)

    Article  ADS  Google Scholar 

  • Li, N., Gong, X., Li, H., et al.: Nonuniform multiview color texture mapping of image sequence and three-dimensional model for faded cultural relics with sift feature points. J. Electron. Imaging 27(1), 011012 (2018)

    ADS  Google Scholar 

  • Macit, M., Gungor, V.C., Tuna, G.: Comparison of QoS-aware single-path versus multi-path routing protocols for image transmission in wireless multimedia sensor networks. Ad Hoc Netw. 19, 132–141 (2014)

    Article  Google Scholar 

  • Qi, S.M., Xu, R.: Application of color transfer algorithm in the virtual color restoration of ancient architecture. Appl. Mech. Mater. 321–324, 2291–2295 (2013)

    Article  Google Scholar 

  • Qi, C.R., Su, H., Mo, K., et al.: PointNet: deep learning on point sets for 3D classification and segmentation. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, pp. 77–85 (2017)

  • Rusu, R.B., Cousins, S.: 3D is here: point cloud library (PCL). In: IEEE International Conference on Robotics and Automation, ICRA 2011, Shanghai, China, 9–13 May 2011. IEEE (2011)

  • Sagiroglu, M.S., Erçil, A.: A texture based approach to reconstruction of archaeological finds. In: International Conference on Virtual Reality Eurographics Association (2005)

  • Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)

    Article  Google Scholar 

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

  • Sorkine, O., Cohen-Or, D.: Least-squares meshes. In: Shape Modeling Applications. IEEE (2004)

  • Sun, X., Ma, H., Sun, Y., Liu, M.: A novel point cloud compression algorithm based on clustering. IEEE Robot. Autom. Lett. 4(2), 2132–2139 (2019)

    Article  Google Scholar 

  • Taubin, G., Rossignac, J.: Geometric compression through topological surgery. ACM Trans. Graph. 17(2), 84–115 (1998)

    Article  Google Scholar 

  • Tizhoosh, H.R., Mitcheltree, C., Zhu, S., Dutta, S.: Barcodes for medical image retrieval using autoencoded Radon transform. In: 2016 23rd International Conference on Pattern Recognition (ICPR), Cancun, pp. 3150–3155 (2016)

  • Tsaig, Y., Donoho, D.L.: Extensions of compressed sensing. Signal Process. 86(3), 549–571 (2006)

    Article  Google Scholar 

  • Walsh, N.P., Alba, B.M., Bose, B., et al.: OMP peptide signals initiate the envelope-stress response by activating DegS protease via relief of inhibition mediated by its PDZ domain. Cell 113(1), 61–71 (2003)

    Article  Google Scholar 

  • Wang, X., Geng, G., Li, X., et al.: A cultural relic line drawings generation algorithm based on explicit ridge line. In: 2015 International Conference on Virtual Reality and Visualization (ICVRV). IEEE, pp. 173–176 (2015)

  • Wang, J., Zhang, T., Song, J., et al.: A survey on learning to hash. IEEE Trans. Pattern Anal. Mach. Intell. 40(4), 769–790 (2018)

    Article  Google Scholar 

  • Wu, Q., Yang, H., Wei, M., et al.: Automatic 3D reconstruction of electrical substation scene from LiDAR point cloud. ISPRS J. Photogramm. Remote Sens. 143, 57–71 (2018)

    Article  ADS  Google Scholar 

  • Xiao, S., Lv, Z., Zhou, X.: A lung 3D model reconstruction method based on compressed sensing and MRI. In: 2015 IET International Conference on Biomedical Image and Signal Processing (ICBISP 2015), Beijing, pp. 1–4 (2015)

  • Zhang, Y., Li, K., Chen, X., et al.: A multi feature fusion method for reassembly of 3D cultural heritage artifacts. J. Cult. Herit. 33, 191–200 (2018)

    Article  Google Scholar 

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

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank all the reviewers for their valuable comments. This work has been supported by the grants from the Natural Science Basic Research Program General Project (No. 2019JQ166) and the National Science Foundation (Nos. 2017YFB1402103, 61902317).

Author information

Authors and Affiliations

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

    Xiaoxue Chen, Mingquan Zhou, Linbo Zou, Li Fan, Jiabei Hu, Guohua Geng & Haibo Zhang

Authors
  1. Xiaoxue Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mingquan Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Linbo Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jiabei Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Guohua Geng
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Haibo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haibo Zhang.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, X., Zhou, M., Zou, L. et al. A fast reconstruction method of the dense point-cloud model for cultural heritage artifacts based on compressed sensing and sparse auto-encoder. Opt Quant Electron 51, 322 (2019). https://doi.org/10.1007/s11082-019-2038-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11082-019-2038-y

Keywords

Search

Navigation