Web-Based Real-Time LADAR Data Visualization with Multi-user Collaboration Support

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10850)


In this paper we present a web-based visualization system developed for visualizing real-time point cloud data obtained from LADAR (or other) sensors. The system allows direct visualization of captured data, visualization of data from database or visualization of preprocessed data (e.g. labeled or classified data). The system allows the concurrent visualization from same or different data-sources on multiple clients in the web browser. Due to the use of modern web technologies the client can also be used on mobile devices. The system is developed using modern client- and server-side web technologies. The system allows connection with an existing LADAR sensor grabber applications through use of UDP sockets. Both server- and client-side parts of the system are modular and allow the integration of newly developed modules and designing a specific work-flow scenarios for target end-user groups. The system allows the interactive visualization of datasets with millions of points as well as streaming visualization with high throughput speeds.


LiDAR LADAR Point cloud data WebGL Data visualization 


  1. 1.
    Du, H., Henry, P., Ren, X., Cheng, M., Goldman, D.B., Seitz, S.M., Fox, D.: Interactive 3D modeling of indoor environments with a consumer depth camera. In: Proceedings of the 13th International Conference on Ubiquitous Computing, pp. 75–84. ACM (2011)Google Scholar
  2. 2.
    Haala, N., Peter, M., Kremer, J., Hunter, G.: Mobile LiDAR mapping for 3D point cloud collection in urban areas - a performance test. Int. Arch. Photogrammetry Remote Sens. Spat. Inf. Sci. 37, 1119–1127 (2008)Google Scholar
  3. 3.
    Molebny, V., McManamon, P.F., Steinvall, O., Kobayashi, T., Chen, W.: Laser radar: historical prospective-from the east to the west. Opt. Eng. 56, 56 (2016). Scholar
  4. 4.
    Rosnell, T., Honkavaara, E.: Point cloud generation from aerial image data acquired by a quadrocopter type micro unmanned aerial vehicle and a digital still camera. Sensors 12(1), 453–480 (2012)CrossRefGoogle Scholar
  5. 5.
    Wang, C.C., Thorpe, C., Suppe, A.: LADAR-based detection and tracking of moving objectsfrom a ground vehicle at high speeds. In: IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683), pp. 416–421, June 2003Google Scholar
  6. 6.
    Navarro-Serment, L.E., Mertz, C., Hebert, M.: Pedestrian detection and tracking using three-dimensional LADAR data. Int. J. Robot. Res. 29(12), 1516–1528 (2010)CrossRefGoogle Scholar
  7. 7.
    Rusu, R.B., Cousins, S.: 3D is here: Point cloud library (pcl). In: IEEE International Conference on Robotics and automation (ICRA) 2011, pp. 1–4. IEEE (2011)Google Scholar
  8. 8.
    Kreylos, O., Bawden, G.W., Kellogg, L.H.: Immersive visualization and analysis of LiDAR data. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008. LNCS, vol. 5358, pp. 846–855. Springer, Heidelberg (2008). Scholar
  9. 9.
    Su, T., Wang, W., Lv, Z., Wu, W., Li, X.: Rapid delaunay triangulation for randomly distributed point cloud data using adaptive hilbert curve. Comput. Graph. 54, 65–74 (2016). Special Issue on CAD/Graphics 2015CrossRefGoogle Scholar
  10. 10.
    Hug, C., Krzystek, P., Fuchs, W.: Advanced lidar data processing with lastools. In: ISPRS Congress, pp. 12–23 (2004)Google Scholar
  11. 11.
    van Oosterom, P., Martinez-Rubi, O., Ivanova, M., Horhammer, M., Geringer, D., Ravada, S., Tijssen, T., Kodde, M., Gonçalves, R.: Massive point cloud data management. Comput. Graph. 49(C), 92–125 (2015)CrossRefGoogle Scholar
  12. 12.
    Lavrič, P., Bohak, C., Marolt, M.: Collaborative view-aligned annotations in web-based 3D medical data visualization. In: MIPRO 2017, 40th Jubilee International Convention, 22–26 May 2017, Opatija, Croatia, proceedings, pp. 276–280 (2017)Google Scholar
  13. 13.
    Schütz, M.: Potree: Rendering Large Point Clouds in Web Browsers. Master’s thesis, Institute of Computer Graphics and Algorithms, Vienna, University of Technology, Favoritenstrasse 9–11/186, A-1040 Vienna, Austria, September 2016Google Scholar
  14. 14.
    Marion, C., Jomier, J.: Real-time collaborative scientific WebGL visualization with WebSocket. In: Proceedings of the 17th International Conference on 3D Web Technology, pp. 47–50. ACM (2012)Google Scholar

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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia
  2. 2.School of Electronics EngineeringKyungpook National UniversityBuk-gu, DaeguKorea

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