Volume XL-5
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5, 615-620, 2014
https://doi.org/10.5194/isprsarchives-XL-5-615-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-5, 615-620, 2014
https://doi.org/10.5194/isprsarchives-XL-5-615-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

  06 Jun 2014

06 Jun 2014

Filtering of Point Clouds from Photogrammetric Surface Reconstruction

K. Wenzel, M. Rothermel, D. Fritsch, and N. Haala K. Wenzel et al.
  • University of Stuttgart, Institute for Photogrammetry, Stuttgart, Germany

Keywords: Point Clouds, Photogrammetry, Laser Scanning, Surface Reconstruction, Filtering, Big Data

Abstract. The density and data volumes for recorded 3D surfaces increase steadily. In particular during photogrammetric surface reconstruction and laser scanning applications these volumes often exceed the limits of the available hardware and software. The large point clouds and meshes acquired during the projects contain billions of vertices and require scalable data handling frameworks for further processing. Beside the scalability to big data, these methods also should adapt to non-uniform data density and precision resulting from varying acquisition distances, as required for data from Photogrammetry and Laser Scanning. For this purpose, we present a framework called Pine Tree, which is based on an out-of-core octree. It enables fast local data queries, such as nearest neighbor queries for filtering, while dynamically storing and loading data from the hard disk. This way, large amounts of data can be processed on limited main memory. Within this paper, we describe the Pine Tree approach as well as its underlying methods. Furthermore, examples for a filtering task are shown, where overlapping point clouds are thinned out by preserving the locally densest point cloud only. By adding an optional redundancy constraint, point validation and outlier rejection can be applied.