Nothing Special   »   [go: up one dir, main page]

Skip to main content
Log in

Point cloud inpainting with normal-based feature matching

  • Regular Paper
  • Published:
Multimedia Systems Aims and scope Submit manuscript

Abstract

With the development of LiDAR technology, point cloud as a data format for representing 3D objects has become more and more widely used. However, negative factors like occlusion or the unfavorable properties of the material surface will lead to the presence of geometric deficiencies, which usually exhibit as holes in point clouds. To solve this kind of problem, point cloud inpainting is proposed. In this paper, we propose an improved point cloud inpainting method, which searches for the proper context for the detected hole and then adopts a more efficient feature matching strategy to refine data source in the hole-filling step by aligning a set of corresponding feature points. Experimental result with comparisons demonstrates its competitive effectiveness with an average 14% gain in GPSNR and better inpainting quality from a subjective perspective.

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

Access this article

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Sinh, N. V., Ha, T. M., Thanh, N. T.: Filling holes on the surface of 3D point clouds based on tangent plane of hole boundary points. Symposium on Information and Communication Technology, pp. 331–338. ACM. (2016)

  2. Doria, D., Radke, R. J.: Filling large holes in LiDAR data by inpainting depth gradients. 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. IEEE. (2012)

  3. Sahay, P., Rajagopalan A.N.: Harnessing self-similarity for reconstruction of large missing regions in 3D models. International Conference on Pattern Recognition, pp. 101–104. IEEE. (2012)

  4. Sahay, P., Rajagopalan, A. N.: Geometric inpainting of 3D structures. Computer Vision and Pattern Recognition Workshops, pp. 1–7. IEEE. (2015)

  5. Lozes, F., Elmoataz, A., Lezoray, O.: PDE-based graph signal processing for 3-D color point clouds: opportunities for cultural heritage. IEEE Signal Processing Magzine. 32(4), 103–111 (2015)

    Article  Google Scholar 

  6. Dinesh, C., Bajic, I. V., Cheung, G.: Exemplar-based framework for 3D point cloud hole filling. IEEE Visual Communications and Image Processing. IEEE. (2017)

  7. Wang, J., Oliveira, M.M.: Filling holes on locally smooth surfaces reconstructed from point clouds. Image Visual Computer. 25(1), 103–113 (2007)

    Article  Google Scholar 

  8. Sagawa, R., Ikeuchi, K.: Hole filling of a 3D model by flipping signs of a signed distance field in adaptive resolution. IEEE Transactions on Pattern Analysis and Machine Intelligence. 30(4), 686–699 (2008)

    Article  Google Scholar 

  9. Hongbin, L., Wei, W.: Feature preserving holes filling of scattered point cloud based on tensor voting. IEEE International Conference on Signal and Image Processing. IEEE. (2017)

  10. Setty, S., Ganihar, S. A., Mudenagudi, U.: Framework for 3D object hole filling. Fifth National Conference on Computer Vision. IEEE. (2015)

  11. Dinesh, C., Bajic, I.V., Cheung, G.: Adaptive non-rigid inpainting of 3d point cloud geometry. IEEE Signal Processing. Letter. 25(6), 878–882 (2018)

    Article  Google Scholar 

  12. Chalmoviansky, P., Jttler, B.: Filling holes in point clouds. Mathematics of surfaces, pp. 196–212. Springer, Berlin (2003)

    MATH  Google Scholar 

  13. Wu, X., Chen, W.: A scattered point set hole-filling method based on boundary extension and convergence. Intelligent Control and Automation (WCICA), 2014 11th World Congress, pp. 5329–5334. IEEE. (2014)

  14. Wei Z, Zongming G.: Local frequency interpretation and non-local self-similarity on graph for point cloud inpainting. IEEE Transactions on Image Processing, 28(8), pp. 4087–4100 (2019)

  15. Lozes, F., Elmoataz, A., Lezoray, O.: Partial difference operators on weighted graphs for image processing on surfaces and point clouds. IEEE Transaction Image Processsing. 23(9), 3896–3909 (2014)

    Article  MathSciNet  Google Scholar 

  16. Carmelo, M., Gareth, P.S., Rahul, S.: Novel algorithms for 3d surface point cloud boundary detection and edge reconstruction. Journal of Computational Design & Engineering. 1, 1 (2018)

    Google Scholar 

  17. Zhu, R.F., Fang, Y.: Boundary extraction and simplification of scattered point cloud based on improved single-axis searching method. Science Technology and Engeering. (2012)

  18. Arun, K.S., Huang, T.S., Blostein, S.D.: Least-squares fitting of 2 3-d point sets. IEEE Transactions on Pattern Analysis and Machine Intelligence. PAMI-9(5), 698–700 (1987)

    Article  Google Scholar 

  19. http://plenodb.jpeg.org. Accessed 21 Oct 2021

Download references

Funding

This work was funded by the National Key R&D Program of China under grant no. 2019YFB1802904.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chuanchuan Yang.

Additional information

Communicated by C. Yan.

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

Shi, Y., Yang, C. Point cloud inpainting with normal-based feature matching. Multimedia Systems 28, 521–527 (2022). https://doi.org/10.1007/s00530-021-00856-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00530-021-00856-9

Keywords

Navigation