Volume XL-4/W4
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4/W4, 7-12, 2013
https://doi.org/10.5194/isprsarchives-XL-4-W4-7-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-4/W4, 7-12, 2013
https://doi.org/10.5194/isprsarchives-XL-4-W4-7-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  26 Nov 2013

26 Nov 2013

Implicit Surface Modeling from Imprecise Point Clouds

E. Funk2,1, L. S. Dooley1, A. Boerner2, and D. Griessbach2 E. Funk et al.
  • 1Department of Computing and Communications, The Open University, Milton Keynes, UK
  • 2Department of Optical Information Systems, Institute of Robotics and Mechatronics, DLR (German Aerospace Center), Berlin, Germany

Keywords: Implicit Surface, General Lasso, Sparse Approximation

Abstract. In applying optical methods for automated 3D indoor modelling, the 3D reconstruction of objects and surfaces is very sensitive to both lighting conditions and the observed surface properties, which ultimately compromise the utility of the acquired 3D point clouds. This paper presents a robust scene reconstruction method which is predicated upon the observation that most objects contain only a small set of primitives. The approach combines sparse approximation techniques from the compressive sensing domain with surface rendering approaches from computer graphics. The amalgamation of these techniques allows a scene to be represented by a small set of geometric primitives and to generate perceptually appealing results. The resulting scene surface models are defined as implicit functions and may be processed using conventional rendering algorithms such as marching cubes, to deliver polygonal models of arbitrary resolution. It will also be shown that 3D point clouds with outliers, strong noise and varying sampling density can be reliably processed without manual intervention.