Full text: Technical Commission III (B3)

IMAGE ACQUISITION CONSTRAINTS FOR 
   
PANORAMIC FRAME CAMERA IMAGING 
H. Kauhanen * * P. Rénnholm? 
* Aalto University School of Engineering, Department of Surveying and Planning, Finland — 
(heikki.kauhanen, petri.ronnholm)@aalto.fi 
Commission III, WG III/5 
KEY WORDS: Close range photogrammetry, Perspective error, Camera geometry, Simulation, Concentric, Eccentric 
ABSTRACT: 
The paper describes an approach to quantify the amount of projective error produced by an offset of projection centres in a panoramic 
imaging workflow. We have limited this research to such panoramic workflows in which several sub-images using planar image sensor 
are taken and then stitched together as a large panoramic image mosaic. The aim is to simulate how large the offset can be before it 
introduces significant error to the dataset. The method uses geometrical analysis to calculate the error in various cases. Constraints for 
shooting distance, focal length and the depth of the area of interest are taken into account. Considering these constraints, it is possible 
to safely use even poorly calibrated panoramic camera rig with noticeable offset in projection centre locations. The aim is to create 
datasets suited for photogrammetric reconstruction. Similar constraints can be used also for finding recommended areas from the image 
planes for automatic feature matching and thus improve stitching of sub-images into full panoramic mosaics. 
The results are mainly designed to be used with long focal length cameras where the offset of projection centre of sub-images can seem 
to be significant but on the other hand the shooting distance is also long. We show that in such situations the error introduced by the 
offset of the projection centres results only in negligible error when stitching a metric panorama. Even if the main use of the results is 
with cameras of long focal length, they are feasible for all focal lengths. 
1. INTRODUCTION 
Panoramic images are considered as images or image sequences 
with large field of view (FoV). Typically, the FoV of panoramic 
images is between 100 degrees to complete 360 degrees. 
Applications of panoramic images are various, such as virtual 
museums (Zara, 2004), virtual travel (Yan et al, 2009), 
architecture visualizations (Hotten and Diprose, 2000), 3D 
object reconstruction (Luhmann and Tecklenburg, 2004), and 
robot navigation (e.g., Yen and MacDonald, 2002; Briggs et al., 
2006), just to name few. 
Several methods for creating panoramic images do exist, such as 
using fish-eye or other large FoV lenses, stitching several sub- 
images (e.g, Deng and Zhang, 2003) collecting data with 
rotating line camera (Huang et al., 2003) or reflecting captured 
image through rotating, spherical, conical, hyperbolic or 
parabolic mirror (e.g., Svoboda et al., 1998; Gaspar and Victor, 
1999; Nakao and Kashitani 2001; Fernandes et al, 2006; Fan 
and Qi-dan, 2009). In addition, similar methods can be used for 
the creation of extremely large images using low resolution 
cameras, even if the FoV does not exceed 100 degrees (Kopf et 
al., 2007). Such approach, typically, requires a long focal length 
(Kauhanen et al, 2009). In this article, however, we are only 
discussing about stitched panoramic images. 
Usually, panoramic imaging in photogrammetric applications 
calls for a tedious calibration setup to eliminate any geometric 
errors. Metric panorama is considered to require a stable 
panoramic rig with precise adjustments to accurately place the 
* Corresponding author. 
projection centre into a correct place. Such concentric imaging 
setup ensures that perspectives of all sub-images are identical 
(Póntinen, 2004). Only then it is possible to construct a 
panoramic image that meets the criteria of an ideal geometry. 
However in all cases, concentric imaging is not possible or even 
desired. For example, if camera clusters are preferred for 
simultaneous sub-image capture, it is physically difficult to make 
such a system that fulfils the requirements of concentric imaging. 
Examples of such camera clusters are Dodeca 2360 camera 
system, OPTAG and DVS Panoramic Viewing System. 
Camera-based geometric distortions of sub-images can be 
calibrated and corrected (Brown, 1971). On the contrary, if sub- 
images are not acquired concentrically, perspective differences 
remain. Such perspective errors cannot be corrected without a 
complete 3D model of the scene. The amount of perspective 
differences defines how well sub-images can be stitched together 
into a seamless panoramic image. In some cases, however, even 
if perspective differences of sub-images are large, stitching can 
be done with acceptable accuracy. 
In this paper, we describe a simulation method to accurately 
quantify the error introduced by offsets of projection centres in a 
panoramic imaging process. The paper is motivated by our 
previous work with long focal length panoramic images where 
the shooting distance was longer than in usual close range 
photogrammetric applications. That work yielded good results 
and we came into conclusion that we need to be able to calculate 
beforehand perspective errors caused by an eccentric rotation of 
projection centres. 
  
    
    
  
  
  
  
  
  
     
    
  
  
  
  
  
  
  
    
   
   
    
    
   
   
   
   
   
   
    
    
   
    
   
   
   
   
   
   
    
   
    
   
   
   
   
   
     
     
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