Full text: XVIIIth Congress (Part B3)

face but to focus on the goal of surface geometries and 
on those methods that are best suited for a particular 
need. As a result one may find today commercial 
systems for modeling smaller industrial objects or 
human faces that rely on non-stereoscopic ranging or 
even tactile reconstruction systems. An industry not 
much smaller than that of photogrammetric equipment 
has recently emerged that will feed the geometry of 
objects into computer graphic rendering systems. 
These may be based on magnetic, acoustic or optical 
tracking of a cursor in 3-D space. It may employ 
ranging, or use structured light. Table 1 is an attempt 
at summarizing the range of modeling techniques for 
3-dimensional objects. 
  
Stereoscopy 
optical and active echo-ranging, electron-microscopes 
Tactile profiling 
magnetic tracking 
optical tracking 
acoustic tracking 
Structured light 
Exploiting geometric constraints in single image 
- Ranging with lasers and radar altimetry 
Interferometry 
Shape-from-Shading 
Use of shadows, layover 
Photometric stereo 
Tomographic imaging 
  
Table 1: Techniques Used for Extracting 
Object Surface Geometry 
1.3 Surface Radiometry 
The extension of reconstruction from a purely geo- 
metric to a global view of surface properties is a result 
of the transition to so-called „Digital Visual Informa- 
tion“ to encompass both ,Image Processing” and 
„Computer Graphics“. The surface consists of the bald 
reference object, objects placed on the bald reference 
surface, information about the surface’s reflective 
properties such as color, specularity and texture. 
Methods of assessing the surface radiometry are well- 
understood in remote sensing. The brightness infor- 
mation obtained in an image needs to be inverted to a 
measure of reflective properties of the surface. This 
benefits from multiple looks at each surface point in 
different spectral channels as well as from different 
vantage points. 
The assessment of texture is most commonly accom- 
plished from photographs. The object geometry is 
being modeled by polygons and each polygon re- 
ceives a photographic facet. This facet needs to be 
made independent of effects of the illumination and 
viewing direction valid at the time of imaging. 
1.4 Rendering a Surface 
Clearly surface reconstruction is a function of its 
application. Traditionally photogrammetry has been 
used for topographic mapping so that the resulting 
maps are available for navigation, orientation and 
planning. With the advent of computer generated 
images and computer graphics the surfaces also are 
being used for rendering. The problem is then ex- 
panded by issues of illumination, viewing position 
and viewing direction. The map as traditional product 
is being replaced by geometric and radiometric object 
  
422 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B3. Vienna 1996 
   
models and by the graphics pipeline, to feed the 
softcopy visualization of an object. 
2 STEREOSCOPIC RECONSTRUCTION 
2.1 A Systems View 
Stereoscopy is the best understood method of surface 
reconstruction in photogrammetry. The traditional 
view decomposes stereoscopic machine vision into a 
set of individual worksteps (Table 2). 
  
Image orientation 
Image matching 
Preliminary surface definition 
Match verification and acceptance 
Final surface representation 
Gridding 
Data formatting 
  
Table 2: The stereoscopic process flow. 
The most widely discussed aspect of stereoscopic 
machine vision is image matching. The least under- 
stood is verification and acceptance of a surface point. 
2.2 Image Matching 
The search for homologue features in overlapping 
images has received most of the attention spent in the 
past on automated digital stereoscopy. A recent 
authoritative presentation is by Forstner (1993). The 
matching domain is being addressed essentially in 
image or object space and in terms of feature matching 
or area-based matching. The last 25 years have seen a 
proliferation of techniques that focus on the speed of 
matches, the robustness and independence of radio- 
metric and geometric disparities in images, the pull-in 
range, i.e. the ability to find a match even if the 
geometric differences between two images are large, 
the smart prediction of presumed match locations, the 
idea of using resolution pyramids in a hierarchical 
approach, the optimization of similarity measures, e.g. 
in terms of a least squares estimation of the match 
location, the combination of geometric and radio- 
metric parameters to determine a match, the reduction 
of the dimensionality of the problem by constraining 
the search areas along epipolar lines. 
The matching ideas further could be grouped into 
those applicable when nothing is known about the 
object and the camera (Zhang et al., 1995); when metric 
cameras are used and the orientation of the cameras in 
3-D space is known; when the object is fully 3-dimen- 
sional and has many hidden and occluded elements; 
when structures exist that can be approximated by 
polygons, e.g. when looking for buildings in an urban 
environment; when the radiometry interferes with geo- 
metry such as in active echo-ranging systems (radar 
and sound). 
The matching accuracy is variously reported as 
ranging between + 2 pixels in highly dissimilar image 
pairs such as those obtained with a speckle-infested 
radar system to + 0.05 pixels or better when sharply 
defined, rotationally symmetric objects can serve as 
homologue features in overlapping, well-illuminated 
stereo photography using retro-targets. 
   
  
  
  
  
  
  
  
  
  
  
  
  
  
  
  
   
    
  
   
  
  
  
   
   
   
   
  
  
  
   
  
   
  
   
  
  
  
  
   
  
   
  
   
  
   
  
   
   
  
  
   
  
   
  
  
  
  
  
   
  
  
  
   
   
   
   
    
   
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