Mason Wang

Image Formation

Week 1, Thursday

Pinhole Cameras

-Why don’t you see an image when you hold up a piece of paper? - Each point in space reflects in all directions, which get spread out across the paper - Conversely, each point on the paper gets light from multiple points in the scene.

We can think of the unflipped image instead:

alt text Can use similar triangles:

Note that depth is \(Z\), not the ray length (the hypotenuse).

\[\mathbf{x} = (x,y) = (X,Y) \cdot \frac{f}{Z}\]

Where \((x,y)\) are the image/pixel coordinates, and \((X,Y)\) are the world coordinates.

Properties Preserved

Example - a line.

\(X(t) = X_0 + at\) \(Y(t) = Y_0 + bt\) \(Z(t) = Z_0 + ct\) After computing $x$ and $y$ in pixel space and taking \(t \rightarrow \infty\), we see that we get points in image space as

\[\frac{fa}{c}\]

\(\frac{fb}{c}\) Which does not depend on $X_0$, or the ‘initial point’ on the line. All parallel lines intersect at the vanishing point, it does not matter the ‘offset’ of the line, just the ‘slope’

Unless the lines are parallel to the image plane, or \(c=0\), parallel lines will remain parallel. Lines that are parallel to the image plane will either have a single intersection or be parallel.

Diffusion models also don’t always have perspective geometry.

Homogeneous Coordinates

The $(X,Y,Z)$ to $(x,y)$ mapping is not linear.

Lets try having

alt text And, all points are equivalent up to scalar multiplications.

alt text

Now, let’s look for a transformation from 3D Homogenous coordinates to 2D image pixel coordinates: alt text

Intrinsics Matrix: alt text

We should also have translations in x-y sspace