Automatic co-registration of terrestrial laser scanner and digital camera for the generation of hybrids models

  1. Diego González Aguilera 1
  2. Pablo Rodríguez Gonzálvez 1
  3. Javier Gómez Lahoz 1
  1. 1 Universidad de Salamanca
    info

    Universidad de Salamanca

    Salamanca, España

    ROR https://ror.org/02f40zc51

Actas:
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

ISSN: 1682-1777

Año de publicación: 2007

Volumen: 36

Parte: 3

Páginas: 162-168

Congreso: ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007

Tipo: Aportación congreso

GOOGLE SCHOLAR lock_openAcceso abierto editor

Resumen

3D modeling and visualization of real world scenes is an important topic of research with applications in many areas such as virtual museums, game and entertainment, architecture description and restoration, virtual reality, archaeology, many industrial applications and last but not least important tourist applications. 3D modeling and visualization are the creation of a computer representation of real world environments that merges together data coming from one or more sensors. The representation of the geometric and texture information of a real scene is a very challenging task due to the acquisition of large-scale data, complexity of the geometry and difficulties to cope with reflectance properties of the objects and variations in the lighting in the scene. Two approaches, depending on the type of sensor (terrestrial laser scanner or digital cameras), are typically used to face the 3D reconstruction problem. Laser scanners provide 3D metric information in real time through an array of coordinates: range images. Digital cameras are used to acquire high-resolution images of the scenes. These images are 2D arrays of reflected light from objects but do not contain any explicit metric information. Further processing is necessary, to calibrate cameras and compute 3D models. This paper aims to demonstrate how active and passive sensors can be registered and combined through a hybrid approach to compute 3D models of complex scenes with photo-realistic quality. Particularly, the proposed approach tries to deal with two different images: a high-resolution image acquired with a digital camera and a range image obtained from a laser scanner model using collinearity condition. Our goal is to devise and implement a robust, automatic, and accurate hybrid-technique for registration of both sensors for efficient modeling (geometry) and rendering (radiometry) of complex environments. To this end, we have developed a novel application for laser scanning which allow us to test the approach developed over experimental results.

Referencias bibliográficas

  • Allen P. K., Troccoli A., Smith B., 2003. New methods for digital modeling of historic sites. IEEE Journal.
  • Burns, J.B., A.R. Hanson, E.M. Riseman, 1986. Extracting straight lines. IEEE transactions on pattern analysis and machine intelligence, Vol. 8 (4).
  • Canny J.F. 1986. A computational approach to edge detection. IEEE Journal.
  • Grün, A., 1985. Adaptive least squares correlation: a powerful image matching technique. S. Afr. J. of Photogrammetry, Remote Sensing and Cartography, 14, 3
  • Domingo Preciado Ana, 2000. Tesis Doctoral. Universidad de Cantabria.
  • Elstrom M., 1998. A Stereo-Based Technique for the Registration of Color and LIDAR images, Master’s Thesis, University of Tennessee, Knoxville
  • Hartley R. and Zisserman A., 2000. Multiple View Geometry in Computer Vision, Cambridge Univ. Press.
  • Kraus, K. 1993. Advanced Methods and Applications. Vol.2. Fundamentals and Standard Processes. Institute for Photogrammetry Vienna University of Technology.
  • Abdel-Aziz YI y Karara HM, 1971. Direct linear transformation from comparator coordinates into space coordinates in close range photogrammetry. Symposium on close range photogrammetry.
  • Aguilera D. G., Lahoz J. G., 2006. Terrestrial laser scanner and high-resolution camera registration through single image-based modeling. Eurographics Book.
  • Aguilera D., Claro-I. M. A., Gomez Lahoz J., Finat J., Gonzalo- T. M., 2004. Development of a simulator for reliable and accurate 3D reconstruction from a single view. ISPRS Congress. Istambul.
  • Al-Manasir K., Fraser C. S., 2006. Automatic registration of terrestrial laser scanner data via imagery. ISPRS Archives. Dresden (Germany).
  • Alshawabkeh Yahya, Norbert Haala, Dieter Fritsch, 2006. 2D- 3D feature extraction and registration of real world scenes. ISPRS Archives. Dresden (Germany).
  • Baarda, W., 1968. A Testing Procedure for use in Geodetic in Networks. Publications on Geodesy, News Series, 2(5), Netherlands Geodetic Commission.
  • Bourke P., 1989. Efficient Triangulation Algorithm Suitable for Terrain Modelling. Pan Pacific Computer Conference, Beijing, China.
  • Fischler, M. A., and R. C. Bolles, 1981. Random sample consensus. CM Communications.
  • Förstner, W. and Gülch, E., 1987. A Fast Operator for Detection and Precise Location of Distinct Points, Corners and Centres of Circular Features. ISPRS Workshop, Interlaken.
  • Ikeuchi, Nakazawa, 2003. Creating virtual buddha statues through observation. IEEE Workshop.
  • Lensch H.P., W. Heidrich, 2001. A silhouette-based algorithm for texture registration and stitching, Graphical Models, vol. 63, n4.
  • Levoy M., K. Pulli, B. Curless, 2000. The digital Michelangelo project: 3D scanning of large statues. Siggraph, CG Proceedings.
  • McAllister D. K., L. Nyland, V. Popescu, A. Lastra, and C. McCue, 1999. Real-time rendering of real world environments. In Rendering Techniques, EuroGraphics Rendering Workshop.
  • Rocchini C., P. Cignomi, C. Montani. Multiple textures stitching and blending on 3D objects, 1999. Eurographics, New York.
  • Stamos I. And P. K. Allen. Automatic registration of 2-D with 3-D imagery in urban environments, 2001. Proceedings ICCV.