# Computer Graphics


Basics of Computer Graphics

## Homogeneous Coordinates

Points and vectors are represented using homogeneous coordinates in computer graphics.
This allows affine transformations in 3D (i.e. rotation and translation) to be represented as a matrix multiplication.
While rotations can typically be represented in a 3x3 matrix multiplication, a translation requires a shear in 4D.

Points are $$\displaystyle (x,y,z,1)$$ and vectors are $$\displaystyle (x,y,z,0)$$.
The last coordinate in points allow for translations to be represented as matrix multiplications.

Notes
• The point $$\displaystyle (kx, ky, kz, k)$$ is equivalent to $$\displaystyle (x, y, z, 1)$$.

Affine transformations consist of translations, rotations, and scaling

### Translation Matrix

$$\displaystyle T = \begin{bmatrix} 1 & 0 & 0 & X\\ 0 & 1 & 0 & Y\\ 0 & 0 & 1 & Z\\ 0 & 0 & 0 & 1 \end{bmatrix}$$

### Rotation Matrix

Rotations can be about the X, Y, and Z axis.
Below is a rotation about the Z axis by angle $$\displaystyle \theta$$.
$$\displaystyle R = \begin{bmatrix} \cos(\theta) & -\sin(\theta) & 0 & 0\\ \sin(\theta) & \cos(\theta) & 0 & 0\\ 0 & 0 & 1 & 0\\ 0 & 0 & 0 & 1 \end{bmatrix}$$

To formulate a rotation about a specific axis, we use Wikipedia:Rodrigues' rotation formula.
Suppose we want to rotate by angle $$\displaystyle \theta$$ around axis $$\displaystyle \mathbf{k}=(k_x, k_y, k_z)$$.
Let $$\displaystyle \mathbf{K} = [\mathbf{k}]_{\times} = \begin{bmatrix} 0 & -k_z & k_y\\ k_z & 0 & -k_x\\ -k_y & k_x & 0 \end{bmatrix}$$
Then the rotation matrix is $$\displaystyle \mathbf{R} = \mathbf{I}_{3} + (\sin \theta)\mathbf{K} + (1 - \cos \theta)\mathbf{K}^2$$
Here the 4x4 form is: $$\displaystyle R = \begin{bmatrix} \mathbf{R} & \mathbf{0}\\ \mathbf{0}^T & 1 \end{bmatrix}$$

### Scaling Matrix

$$\displaystyle S = \begin{bmatrix} X & 0 & 0 & 0\\ 0 & Y & 0 & 0\\ 0 & 0 & Z & 0\\ 0 & 0 & 0 & 1 \end{bmatrix}$$

## MVP Matrices

To convert from model coordinates $$\displaystyle v$$ to screen coordinates $$\displaystyle w$$, you do multiply by the MVP matrices $$\displaystyle w=P*V*M*v$$

• The model matrix $$\displaystyle M$$ applies the transform of your object. This includes the position and rotation. $$\displaystyle M*v$$ is in world coordinates.
• The view matrix $$\displaystyle V$$ applies the transform of your camera. $$\displaystyle V*M*v$$ is in camera or view coordinates.
• The projection matrix $$\displaystyle P$$ applies the projection of your camera, typically an orthographic or a perspective camera. The perspective camera shrinks objects in the distance.

### Model Matrix

Order of matrices
The model matrix is the product of the element's scale, rotation, and translation matrices.
$$\displaystyle M = T * R * S$$

### View Matrix

Reference
Lookat function
The view matrix is a 4x4 matrix which encodes the position and rotation of the camera.
Given a camera at position $$\displaystyle \mathbf p$$ looking at target $$\displaystyle \mathbf t$$ and up vector $$\displaystyle \mathbf u$$.
We can calculate the forward vector (from target to position) as $$\displaystyle \mathbf{f}=\mathbf{p} - \mathbf{t}$$.
We can calculate the right vector as $$\displaystyle \mathbf u \times \mathbf f$$.
Then the view matrix is written as:

r_x r_y r_z 0
u_x u_y u_z 0
f_x f_y f_z 0
p_x p_y p_z 1

Matrix lookAt(camera_pos, target, up) {
forward = normalize(camera - target)
up_normalized = normalize(up)
right = normalize(cross(up, forward)
// Make sure up is perpendicular to forward
up = normalize(cross(forward, right)
m = stack([right, up, forward, camera], 0)
return m
}


### Perspective Projection Matrix

The projection matrix applies a perspective projection based on the field of view of the camera. This is done dividing the x,y view coordinates by the z-coordinate so that further object appear closer to the center. Note that the output is typically in normalized device coordinates $$\displaystyle [-1, 1]\times[-1, 1]$$ rather than image coordinates $$\displaystyle [0, W] \times [0, H]$$.

Notes: In computer vision, this is analogous to the calibration matrix $$\displaystyle K$$. It contains the intrinsic parameters of your pinhole camera such as field of view and focal length. The focal length determines the resolution of your output.

### Inverting the projection

If you have the depth (either z-depth or euclidean depth), you can invert the projection operation.
The idea is to construct a ray from the camera to the pixel on a plane of the viewing frustrum and scale the distance accordingly.

See stackexchange.

### Interpolation

• Flat shading - color is computed for each face/triangle.
• Gourard shading - color is computed for each vertex and interpolated.
• Phong shading - color is computed for each pixel with the normal vector interpolated from each vertex.

### Lambert reflectance

This is a way to model diffuse (matte) materials.

$$\displaystyle I_D = (\mathbf{L} \cdot \mathbf{N}) * C * I_{L}$$

• $$\displaystyle \mathbf{N}$$ is the normal vector.
• $$\displaystyle \mathbf{L}$$ is the vector to the light.
• $$\displaystyle C$$ is the color.
• $$\displaystyle I_{L}$$ is the intensity of light.

### Phong reflection model

This is a way to model specular (shiny) materials.

Here, the image is a linear combination of ambient, diffuse, and specular colors.

If $$\displaystyle \mathbf{N}$$ is the normal vector, $$\displaystyle \mathbf{V}$$ is a vector from the vertex to the viewer, $$\displaystyle \mathbf{L}$$ from the light to the vertex, and $$\displaystyle \mathbf{R}$$ the incident vector (i.e. $$\displaystyle \mathbf{L}$$ rotated 180 around $$\displaystyle \mathbf{N}$$) then

• Ambient is a constant color for every pixel.
• The diffuse coefficient is $$\displaystyle \mathbf{N} \cdot \mathbf{L}$$.
• The specular coefficient is $$\displaystyle (\mathbf{R} \cdot \mathbf{V})^n$$ where $$\displaystyle n$$ is the shininess.

The final color is $$\displaystyle k_{ambient} * ambientColor + k_{diffuse} * (\mathbf{N} \cdot \mathbf{L}) * diffuseColor + k_{specular} * (\mathbf{R} \cdot \mathbf{V})^n * specularColor$$.

Notes
• The diffuse and specular components need to be computed for every visible light source.

### Physically Based

See pbs disney brdf notes and the pbr-book
In frameworks and libraries, these are often refered to as standard materials or in Blender, Principled BSDF.