QUALITY ASSESSMENT OF MAPPING BUILDING TEXTURES FROM INFRARED 
IMAGE SEQUENCES 
    
   
  
L. Hoegner, D. Iwaszczuk, U. Stilla® 
Technische Universitaet Muenchen (TUM), Germany 
Commission III Working Group 5 
KEY WORDS: thermal imagery, relative orientation, image sequences, texture extraction 
ABSTRACT: 
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. 
1. INTRODUCTION 
The analysis and refinement of buildings in urban areas has 
become an imported research field in the last few years (Pu & 
Vosselman, 2009; Mayer & Reznik, 2006). Buildings in city 
models normally consist of simple facade structures and optical 
textures. To refine and automate the extraction of geometry and 
textures in urban scenes different solutions have been proposed 
during the last years (Mayer, 2007; Heinrichs et al., 2008; Lo & 
Quattrochi, 2003; Pollefeys et al., 2008). Those strategies are 
extracted geometry and textures together to form a new building 
or facade and are not merged with an existing geo-referenced 
city model. 
Much urban information cannot be extracted from normal 
optical images but from other optical domains like infrared. 
Ground cameras are recording the irradiation of building 
façades (Klingert, 2006), for the specification of its thermal 
behaviour for thermal building passports. IR data of buildings 
are collected from several photos and analysed directly in the 
acquired images without any geometric or 3d processing. 
The focus of this paper is the integration of pre-known building 
models in the texture extraction process from image sequences 
to improve the textures' quality. There are two reasons to add 
infrared textures to existing building models instead of 
generating a building model and extract the textures from the 
same infrared image source. The geometric resolution of 
infrared images is quite low compared to images in the visible 
domain and geometric features like edges show different 
appearances which lead to problems in correct extraction of 
facade planes. For many buildings, accurate building models 
from databases like CityGML or a building information model 
(BIM) already exist and the given model has to be improved. 
Infrared textures are an additional data source for the extraction 
of windows (Iwaszezuk et al, 2011; Sirmacek et al., 2011). 
Thermal cameras record electromagnetic radiation in the 
invisible infrared (IR) spectra. Thus, surface characteristics of 
object can be detected, that stay invisible in normal visible 
spectra. For recognition of objects with little difference in 
temperature and for identification of small details from distance, 
thermal cameras must be able to resolve temperatures with an 
accuracy of 0,01 Kelvin. High-quality infrared cameras are able 
to record image sequences with standard video frame rate (25 
fps) or even higher. Because of special cooling technique, 
camera optics and the low production numbers, the expenses of 
infrared cameras are very high compared to normal video 
cameras. Today, thermal image data is used for many different 
applications. Typically, IR data of buildings are collected from 
photos and analyzed directly in the acquired images. Bigger 
building parts are acquired by combining several images. The 
results of the analysis are stored in the 2d photos without any 
geometric or 3d processing. This can be a problem, when 
images from different cameras or views are combined and 
stored for further processing. 
In contrast to conventional IR inspection of buildings, in this 
paper an automated strategy is used for texturing an entire 
building model. Narrow streets and the low resolution and small 
field of view of IR cameras are serious problems. Only small 
parts of the building façade are visible in one image. Direct line 
matching of the images and the projection of the model's edges 
is used especially for aerial images (Frueh et al., 2004; Avbelj et 
al., 2010) fails because of the lack of visible facade edges in 
many of the images. In this case we have to deal with the fact, 
that structures found in IR images do not always have 
correspondences in the building model and vice versa. This 
  
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