Zisserman - 1 
Uncalibrated Vision 
Andrew Zisserman 
Robotics Research Group 
Department of Engineering Science 
University of Oxford 
Oxford 0X1 3PJ, UK. 
1 Introduction 
Uncalibrated vision refers to a body of techniques whose starting point is that a 
camera is a projective sensor, modelled as a linear map between 3-space and the 
image. In particular the camera calibration (the interior orientation, or intrinsic 
parameters) is unknown. Subsequent computations are based solely on projective 
geometry (intersection of lines with planes, for example) as opposed to Euclidean 
geometry where angles can be measured. 
In contrast, the starting point for conventional photogrammetry (and much of 
computer vision) is to precisely model and determine the interior orientation of the 
camera. The camera is then modelled as a Euclidean sensor, able to measure the 
angle between rays. 
The uncalibrated approach is a necessity in many computer vision applications. 
There are often cases where calibration information is not available (at least ini 
tially), for example video sequences or archive photographs, or cases where an initial 
calibration will be lost, for example a camera mounted (and shaken) on a moving 
robot arm or AGV, or deliberate change of focal length (zooming). It might be pos 
sible to guess values for the internal parameters, or parameterise their change (under 
zooming). However, as will be demonstrated in the sequel, calibration is simply not 
necessary for many important applications and tasks. Furthermore, where projec 
tive geometry is insufficient for a task, uncalibrated vision makes explicit the precise 
geometric strata (affine, metric) required. The approach is in concord with the 
‘Active Vision’ paradigm [4] of computer vision, where only sufficient information 
necessary for the task in hand is extracted from the image or image sequence. 
Where affine or metric information is required, the flavour of the computation 
again differs from that of the photogrammetrist. A traditional approach would 
solve directly for calibration (using the DLT for example) or employ particular scene 
knowledge (such as the Euclidean coordinates of a number of points, or that certain 
lines are parallel). In contrast the uncalibrated approach harnesses additional knowl 
edge of the motion (it may be pure translational for example) or camera to generate