Skip to main content
Springer Nature Link
Log in

A fast fusion method for multi-videos with three-dimensional GIS scenes

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Techniques for the fusion of real-world videos with virtual scenes are key to the augmentation of three-dimensional (3-D) virtual geographic scenes, which greatly enhances the immersive visual experience. When a 3-D scene is updated dynamically, the existing video projection-based method for real-virtual fusion is generally slow and inefficient, as all rendered objects must be traversed in the new scene to identify the objects to be fused in the user’s new field of view (FOV). To address this issue, a fast, topology-accounting method for multi-video fusion with 3-D geographic information system (GIS) scenes is proposed. First, the topological models for video object and rendered object are constructed, respectively. Second, by using the topological models, a method that considering topological relationships is proposed to realize rapid identification of rendered objects during the dynamic update of 3-D scenes. Finally, real video and 3-D scene data in Tengzhou City were used to validate the method proposed in this paper. The experiments demonstrated that the method is fast and efficient in the fusion of videos with 3-D GIS scenes, and the computational cost of the proposed method is significantly lower than that of the current method. The proposed method is highly viable and robust, facilitating the fusion of videos with virtual environments.

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

References

  1. Chen, C-F, Bolas, M, Suma, E (2016). Real-time 3D rendering using depth-based geometry reconstruction and view-dependent texture mapping. Siggraph ‘16 ACM SIGGRAPH

  2. Clarke B (1981) A calculus of individuals based on “connection”. Notre Dame Journal of Formal Logic 22(3):204–218

    Article  MathSciNet  Google Scholar 

  3. Corral-Soto, ER Tal, R, Wang, Let al (2012). “3D town: the automatic urban awareness project,” in Computer and Robot Vision (CRV), 2012 Ninth Conference on. IEEE, 2012, pp. 433–440

  4. De Haan G, Piguillet H, Post FH (2010) Spatial navigation for context-aware video surveillance. IEEE Comput Graph Appl 30(5):20–31

    Article  Google Scholar 

  5. Decamp, P, Shaw, G, Kubat, R, et al (2010). An immersive system for browsing and visualizing surveillance video[C]// Acm international conference on multimedia. ACM

  6. Everitt, C (2001). Projective texture mapping. White Paper, nVidia Corporation. Available from: http:// developer.download.nvidia.com/assets/gamedev/docs/projective_texture_mapping.pdf

  7. Ghadirian P, Bishop ID (2008) Integration of augmented reality and GIS: a new approach to realistic landscape visualization. Landsc Urban Plan 86(3–4):226–232

    Article  Google Scholar 

  8. Gong J, Zhu Q, Zhang H et al (2011) An adaptive control method of LODs for 3D scene based on R-tree index. Acta Geodaetica et Cartographica Sinica 40(4):531–534

    Google Scholar 

  9. Haan, GD, Scheuer, J, Vries, RD, et al (2009). Egocentric navigation for video surveillance in 3D Virtual Environments.[C]// IEEE Symposium on 3d User Interfaces. IEEE

  10. Hu JH (2009) Intergrating complementary information for photorealistic representation [D]. University of Southern California, Los Angeles

    Google Scholar 

  11. Jian H, Fan X (2014) Multiple video texture mapping and fusion based on OpenGL shading language. Computer Engineering and Design 35(11):3873–3878

    Google Scholar 

  12. Jian HD, Liao JJ, Fan XT et al (2017) Augmented virtual environment: fusion of real-time video and 3D models in the digital earth system. International Journal of Digital Earth 10(12):1177–1196

    Article  Google Scholar 

  13. Lewis P, Fotheringham S, Winstanley A (2011) Spatial video and GIS. Int J Geogr Inf Sci 25(5):697–716

    Article  Google Scholar 

  14. Liu Z, Li C, Wu P et al (2018) A Level Constraints Removed Algorithm for Avoiding Crack in Massive 3D Terrain. Bulletin of Surveying and Mapping 496(07):52–56

    Google Scholar 

  15. Ma Y, Zhao G, He B (2012) Design and Implicayion of a fused system with 3DGIS and multiple-videos. Computer Applications and Software 29(6):109–112

    Google Scholar 

  16. Milosavljević A, Dimitrijević A, Rančić D (2010) GIS-augmented video surveillance. Int J Geogr Inf Sci 24(9):1415–1433

    Article  Google Scholar 

  17. Milosavljević A, Rančić D, Dimitrijević A et al (2016) Integration of GIS and video surveillance. Int J Geogr Inf Sci 30(10):2089–2107

    Google Scholar 

  18. Mower JE (2009) Creating and delivering augmented scenes. Int J Geogr Inf Sci 23(8):993–1011

    Article  Google Scholar 

  19. Neumann, U, You, S, Hu, J, et al (2003). IEEE Virtual Reality. Proceedings. - Augmented virtual environments (AVE): dynamic fusion of imagery and 3D models[C]// IEEE Virtual Reality. IEEE Computer Society, 2003:61–67

  20. Pan, C., Chen, Y., Wang, G. (2016). Virtual-real fusion with dynamic scene from videos[C]// 2016 international conference on Cyberworlds (CW). IEEE Computer Society

  21. Sawhney, HS, Arpa, A, Kumar, R, et al. (2002). Video flashlights: real time rendering of multiple videos for immersive model visualization[C]// proceedings of the 13th Eurographics workshop on rendering techniques, Pisa, Italy, June 26-28

  22. Segal M, Korobkin C, Van WR et al (1992) Fast shadows and lighting effects using texture mapping. ACM SIGGRAPH Computer Graphics 26(2):249–252

    Article  Google Scholar 

  23. Stephen H, Samarasekera S, Kumar R et al. (2000) Pose estimation, model refinement, and enhanced visualization using video[C]. IEEE Conference on Computer Vision and Pattern Recognition. IEEE 1:488–495

  24. Wan T, Du S, Cui W et al (2020) Robust rigid registration algorithm based on correntropy and bi-directional distance. IEEE Access 8:22225–22234

    Article  Google Scholar 

  25. Wang Y, Krum DM, Coelho EM, Bowman DA (2007) Contextualized videos: combining videos with environment models to support situational understanding. IEEE Trans Vis Comput Graph 13(6):1568–1575

    Article  Google Scholar 

  26. Wang X, Wang Y (2017) Organization and scheduling of indoor three-dimensional geometric model based on spatial topological relation. Geomatics and Information Science of Wuhan University 42(1):35–42

    Google Scholar 

  27. Wu Z, Chen H, Du S et al (2019) Correntropy based scale ICP algorithm for robust point set registration. Pattern Recogn 93:14–24

    Article  Google Scholar 

  28. Ying S, Peng J, Du S et al (2009) A scale stretch method based on ICP for 3D data registration. IEEE Trans Autom Sci Eng 6(3):559–565

    Article  Google Scholar 

  29. Zheng G, Mo L (2003) The action of GIS map-layer on spatial data editing, management and analysis. Science of Surveying and Mapping 28(3):71–73

    Google Scholar 

  30. Zhou Y, Meng M, Wu W et al (2018) Virtual-reality video fusion system based on video model. Journal of System Simulation 30(07):133–140

    Google Scholar 

  31. Zhu Q, Chen X, Ding Y et al (2017) Organization and scheduling method of 3D urban scene data driven by visual perception. Journal of Southwest Jiaotong University 52(05):869–876

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Chinese Academy of Surveying and mapping, Beijing, 100830, China

    Chengming Li, Zhendong Liu, Zhanjie Zhao & Zhaoxin Dai

Authors
  1. Chengming Li
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Zhendong Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Zhanjie Zhao
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Zhaoxin Dai
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Zhaoxin Dai.

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

Li, C., Liu, Z., Zhao, Z. et al. A fast fusion method for multi-videos with three-dimensional GIS scenes. Multimed Tools Appl 80, 1671–1686 (2021). https://doi.org/10.1007/s11042-020-09742-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-020-09742-4

Keywords