Volume XLII-4/W10 | Copyright
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-4/W10, 135-142, 2018
https://doi.org/10.5194/isprs-archives-XLII-4-W10-135-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  12 Sep 2018

12 Sep 2018

SEGMENTATION OF 3D PHOTOGRAMMETRIC POINT CLOUD FOR 3D BUILDING MODELING

E. Özdemir and F. Remondino E. Özdemir and F. Remondino
  • 3D Optical Metrology, Bruno Kessler Foundation (FBK), Trento, Italy

Keywords: point clouds, aerial photogrammetry, segmentation, classification, 3D building reconstruction

Abstract. 3D city modeling has become important over the last decades as these models are being used in different studies including, energy evaluation, visibility analysis, 3D cadastre, urban planning, change detection, disaster management, etc. Segmentation and classification of photogrammetric or LiDAR data is important for 3D city models as these are the main data sources, and, these tasks are challenging due to their complexity. This study presents research in progress, which focuses on the segmentation and classification of 3D point clouds and orthoimages to generate 3D urban models. The aim is to classify photogrammetric-based point clouds (>30pts/sqm) in combination with aerial RGB orthoimages (~10cm, RGB image) in order to name buildings, ground level objects (GLOs), trees, grass areas, and other regions. If on the one hand the classification of aerial orthoimages is foreseen to be a fast approach to get classes and then transfer them from the image to the point cloud space, on the other hand, segmenting a point cloud is expected to be much more time consuming but to provide significant segments from the analyzed scene. For this reason, the proposed method combines segmentation methods on the two geoinformation in order to achieve better results.

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