Finding the linear mapping between homogeneous coordinates

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SUMMARY

The discussion focuses on deriving the linear mapping between homogeneous coordinates in the context of an affine camera projection. The governing equation involves a 2x3 matrix A and a 2x1 vector b, which together define the relationship between world coordinates (X, Y, Z) and image coordinates (x, y). The solution involves constructing a 3x4 projection matrix that incorporates both the identity matrix and the displacement vector. The key takeaway is the necessity to understand how the affine camera's projection differs from perspective projection, particularly in handling the scale factor and the additional displacement.

PREREQUISITES
  • Understanding of affine camera models
  • Familiarity with homogeneous coordinates
  • Knowledge of matrix operations
  • Basic principles of computer vision
NEXT STEPS
  • Study the derivation of the 3x4 projection matrix in affine camera models
  • Learn about the differences between affine and perspective projections
  • Explore the implications of displacement vectors in camera transformations
  • Investigate applications of homogeneous coordinates in computer vision
USEFUL FOR

This discussion is beneficial for computer vision researchers, students studying camera models, and professionals working with image processing and 3D graphics who need to understand the mathematical foundations of camera projections.

stephchia
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Homework Statement


If I have an affine camera with a projection relationship governed by:

\begin{equation}
\begin{bmatrix}
x & y
\end{bmatrix}^T = A
\begin{bmatrix}
X & Y & Z
\end{bmatrix}^T + b
\end{equation}
where A is a 2x3 matrix and b is a 2x1 vector. How can I form a matrix representing the linear mapping between the world point (X,Y,Z) and image point (x,y) if they are represented by homogeneous vectors?

Homework Equations


NIL

The Attempt at a Solution


I understand that for a camera perspective projection, the linear mapping between homogeneous coordinates where the equation is only up to a scale factor can be written as a 3x4 projection matrix that represents a map from 3D to 2D.
\begin{equation}
\begin{bmatrix}
x \\
y \\
w
\end{bmatrix}=
\begin{bmatrix}
f & 0 & 0 & 0 \\
0 & f & 0 & 0 \\
0 & 0 & 1 & 0
\end{bmatrix}
\begin{bmatrix}
X_c \\
Y_c \\
Z_c \\
1
\end{bmatrix}
\end{equation}
However, I am unclear as to how an affine camera differs and how to fit a linear mapping based on the above given governing equation.
 
Physics news on Phys.org
Remember you want your original vector plus the displacement. Start with the identity matrix components in the first three columns and then ask what effect a non-zero value in the fourth column will have.
 

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