QUALITY ASSESSMENT OF MAPPING BUILDING TEXTURES FROM INFRARED IMAGE SEQUENCES

Other literature type English OPEN
Hoegner, L. ; Iwaszczuk, D. ; Stilla, U. (2012)
  • Journal: (issn: 2194-9034, eissn: 2194-9034)
  • Related identifiers: doi: 10.5194/isprsarchives-XXXIX-B3-391-2012
  • Subject:
    acm: ComputingMethodologies_COMPUTERGRAPHICS | ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION

Generation and texturing of building models is a fast developing field of research. Several techniques have been developed to extract building geometry and textures from multiple images and image sequences. In this paper, these techniques are discussed and extended to automatically add new textures from infrared (IR) image sequences to existing building models. In contrast to existing work, geometry and textures are not generated together from the same dataset but the textures are extracted from the image sequence and matched to an existing geo-referenced 3D building model. The texture generation is divided in two main parts. The first part deals with the estimation and refinement of the exterior camera orientation. Feature points are extracted in the images and used as tie points in the sequence. A recorded exterior orientation of the camera s added to these homologous points and a bundle adjustment is performed starting on image pairs and combining the hole sequence. A given 3d model of the observed building is additionally added to introduce further constraint as ground control points in the bundle adjustment. The second part includes the extraction of textures from the images and the combination of textures from different images of the sequence. Using the reconstructed exterior camera orientation for every image of the sequence, the visible facades are projected into the image and texture is extracted. These textures normally contain only parts of the facade. The partial textures extracted from all images are combined to one facade texture. This texture is stored with a 3D reference to the corresponding facade. This allows searching for features in textures and localising those features in 3D space. It will be shown, that the proposed strategy allows texture extraction and mapping even for big building complexes with restricted viewing possibilities and for images with low optical resolution.
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