Full text: XVIIIth Congress (Part B3)

  
  
  
  
  
   
  
   
   
   
  
  
  
  
  
  
  
  
   
  
  
  
   
   
   
  
  
   
   
   
   
    
   
   
   
   
   
   
   
  
   
   
   
   
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2.3 Verification and Quality Control 
The most significant limitation to successful stereo- 
scopic machine vision is one's ability to reliably 
verify and accept a surface point. The effort to deter- 
mine that a match point is consistent with the expec- 
tations and with neighboring match-points often ex- 
ceeds by far the effort to obtain a match at all. The 
success in identifying match-points is traditionally 
being improved by looking for matches at selected 
image locations obtained through interest operators. 
Yet, these concepts have not been able to produce 
working stereoscopic machine vision systems that 
will operate as reliably as a human operator can, except 
for very well constrained scenarios. 
3 OBJECTS IN SINGLE IMAGES 
3.1 An Applications Scenario 
The advent of computer graphics and digital enter- 
tainment as well as a refined need for disaster pre- 
paredness, training and simulation, planning and citi- 
zen participation is creating a market for fully 3- 
dimensional geographic information ^ systems of 
urban areas. The ,CyberCity“ needs tools to recon- 
struct roofs and buildings (Gruber et al, 1995). It is 
not necessary to rely on stereoscopic machine vision 
to reconstruct buildings and building boxes from 
photographs. Braun (1993) has shown that the 
knowledge that a building's walls are vertical and that 
rooflines typically are horizontal can be exploited 
when using a single aerial photograph. This process of 
identifying building boxes and attaching roof shapes 
from single images revives traditional geometric con- 
cepts that were originally developed at the turn of the 
century. At the current time, however, buildings can be 
extracted automatically from single aerial photo- 
graphs or under minimal manual control. 
3.2 GIS and Aerial Images 
In industrial urban areas one can depend on the 
availability of a 2-dimensional geographic informa- 
tion system in which the third dimension may be 
available as an attribute. Therefore one will know the 
footprint of buildings and one will have an elevation 
measure attaclied to each building. This can be used to 
project the footprint and a prediction of the roofline of 
a building into an exterially oriented aerial photo- 
graph. The photograph itself then serves to first verify 
the geometric information in the GIS and second to 
improve the geometry and detail of its information. 
Gruber et al. (1995) have shown a process by which a 
2-dimensional GIS and single images can serve to 
develop building boxes in a process that is similar to 
the work of Braun (1993), but includes the informa- 
tion of the GIS and is automated. 
3.3 Structured Light 
Of course the application scenario with ,,CyberCity" is 
not the only one in which constraints about the ob- 
jects permit one to rely on single images. They can 
also be used when combined with a proper illumi- 
nation system to reconstruct surface shapes. This 
classical approach in machine vision is used in in- 
dustrial applications. Structured light may employ a 
sequence of parallel planes of light and dark that are 
projected onto a surface. If it is plane then the 
International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B3. Vienna 1996 
intersection with the planes of light result in straight 
lines. Deviations from straight lines are a measure of a 
deviation of the surface from a plane. Successful 
applications of structured light sometimes also com- 
bine it with stereoscopy. This may on one hand help in 
resolving ambiguities, and it may on the other hand 
improve the accuracy. Structured light's limitations 
may be the difficulty of obtaining well-defined transi- 
tions between light and dark. 
4 RANGING 
4.1 Laser Ranging and Radar Altimetry 
Ranging is applicable in the robotics environment 
when the distance to an object needs to be known. The 
planetary guidance of a landing spacecraft may use 
ranging to determine the absence of obstacles. An 
automated vehicle may determine its distance from an 
object by laser ranging. Systems exist that will use a 
single point source and scan the object along profiles. 
Multiple sources may be used to scan multiple 
profiles. The concept of laser radar“ measures the run- 
time from the source to the object and the direction in 
to which the energy was transmitted and from which 
echoes were received. These ideas are also the topic of 
NASA-concepts for a satellite-based topographic 
mapping system. The result of a laser range finder is a 
collection of surface points that are irregularly spaced 
and potentially noisy. Ranging of course is also the 
subject of altimetry. This is the traditional planetary 
tool to determine the shape of a surface. On Earth 
altimetry addresses the shape of the water bodies. The 
footprint of a radar altimeter is fairly large and one 
typically assumes that the first echo to arrive at the 
antenna is from the nadir. This assumption is only 
correct if the surface is smooth. In accentuated terrain 
the first echo may be from a point off to the side. 
Planetary altimetry suffers from ambiguities such as 
those obtained on Venus with the Pioneer and 
Magellan missions, when observations are made in 
mountainous terrain. 
4.2 From Points to Surfaces 
Ranging as a data collection mechanism needs to be 
complemented by a resampling technique that con- 
verts the surface points to a continuous surface. Two 
issues exist: first, that the noise in the data be filtered 
and that a smooth surface results from a rough point 
cloud; second, that a data structure comes into 
existence that actually represents the surface as 
opposed to an unstructured collection of points. The 
transition from points to surfaces is trivial if the 
underlying object is smooth and if the object re- 
presents a 2.5-dimensional situation. The problem 
becomes difficult when a fully 3-dimensional situa- 
tion exists. We will discuss this further in Section 9. 
5 SHAPE FROM SHADING 
5.1 Basic Idea 
Shape-from-shading is a traditional technique of ma- 
chine vision to obtain information about the local 
surface shape given the variations in image bright- 
ness. It has traditionally been applied in a controlled 
illumination environment, e.g. when a robot has to 
select among various objects presented in a bin. 
    
  
    
   
    
    
  
  
  
   
    
    
  
  
   
    
 
	        
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