ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W4, 41-46, 2015
https://doi.org/10.5194/isprsannals-II-3-W4-41-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
 
11 Mar 2015
FAST AND ADAPTIVE SURFACE RECONSTRUCTION FROM MOBILE LASER SCANNING DATA OF URBAN AREAS
M. Gordon, M. Hebel, and M. Arens Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Ettlingen, Germany
Keywords: LIDAR, Mapping, Mobile laser scanning, Urban, Surface reconstruction Abstract. The availability of 3D environment models enables many applications such as visualization, planning or simulation. With the use of current mobile laser scanners it is possible to map large areas in relatively short time. One of the emerging problems is to handle the resulting huge amount of data. We present a fast and adaptive approach to represent connected 3D points by surface patches while keeping fine structures untouched. Our approach results in a reasonable reduction of the data and, on the other hand, it preserves details of the captured scene. At all times during data acquisition and processing, the 3D points are organized in an octree with adaptive cell size for fast handling of the data. Cells of the octree are filled with points and split into subcells, if the points do not lie on one plane or are not evenly distributed on the plane. In order to generate a polygon model, each octree cell and its corresponding plane are intersected. As a main result, our approach allows the online generation of an expandable 3D model of controllable granularity. Experiments have been carried out using a sensor vehicle with two laser scanners at an urban test site. The results of the experiments show that the demanded compromise between data reduction and preservation of details can be reached.
Conference paper (PDF, 12775 KB)


Citation: Gordon, M., Hebel, M., and Arens, M.: FAST AND ADAPTIVE SURFACE RECONSTRUCTION FROM MOBILE LASER SCANNING DATA OF URBAN AREAS, ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., II-3/W4, 41-46, https://doi.org/10.5194/isprsannals-II-3-W4-41-2015, 2015.

BibTeX EndNote Reference Manager XML