COMP3145›Homogeneous coordinates and concatenation

HCI & Computer GraphicsTopic 56 of 73

Homogeneous coordinates and concatenation

5 minread

841words

Beginnerlevel

1. Homogeneous Coordinates

Definition: Homogeneous coordinates are a system of coordinates used in computer graphics that allows affine transformations (translation, rotation, scaling, shear) to be represented as matrix multiplications.

In 2D: a point $(x, y)$ is represented as $(x, y, 1)$
In 3D: a point $(x, y, z)$ is represented as $(x, y, z, 1)$

Why use homogeneous coordinates?

Translation can be represented as matrix multiplication, like other linear transformations
Allows concatenation of multiple transformations into a single matrix
Simplifies computations in graphics pipelines

Example (2D Translation): Point $P = (x, y)$ Translation by $(tx, ty)$ :

T = \begin{bmatrix} 1 & 0 & tx \ 0 & 1 & ty \ 0 & 0 & 1 \end{bmatrix}, \quad P' = T \cdot \begin{bmatrix} x \ y \ 1 \end{bmatrix} = \begin{bmatrix} x + tx \ y + ty \ 1 \end{bmatrix}

2. Concatenation of Transformations

Definition: Concatenation is the process of combining multiple transformations into a single matrix by matrix multiplication.

Key Points:

If you have multiple transformations (e.g., scale, then rotate, then translate), you can concatenate them:

M_{\text{final}} = T \cdot R \cdot S

Then apply the single matrix $M_{\text{final}}$ to the point:

P' = M_{\text{final}} \cdot P

This reduces computation and simplifies transformation pipelines.

Important:

Order matters! Matrix multiplication is not commutative.
- Example: Translate → Rotate ≠ Rotate → Translate

3. Example in 2D

Suppose we want to scale, rotate, and translate a point $P = (2, 3)$ :

Scale: $Sx = 2, Sy = 3$

S = \begin{bmatrix} 2 & 0 & 0 \ 0 & 3 & 0 \ 0 & 0 & 1 \end{bmatrix}

Rotate: θ = 90°

R = \begin{bmatrix} 0 & -1 & 0 \ 1 & 0 & 0 \ 0 & 0 & 1 \end{bmatrix}

Translate: $tx = 5, ty = 2$

T = \begin{bmatrix} 1 & 0 & 5 \ 0 & 1 & 2 \ 0 & 0 & 1 \end{bmatrix}

Concatenated Transformation:

M_{\text{final}} = T \cdot R \cdot S

Apply to point:

P' = M_{\text{final}} \cdot \begin{bmatrix} 2 \ 3 \ 1 \end{bmatrix}

This gives the final transformed position in one step.

4. Advantages of Homogeneous Coordinates and Concatenation

All transformations (translation, rotation, scaling, shear) are matrix multiplications.
Enables easy combination of multiple transformations.
Essential in graphics pipelines, animation, and modeling.
Facilitates 3D transformations and projections.

5. Summary

Concept	Definition	Benefit
Homogeneous coordinates	Extend points with an extra dimension (x, y, 1) or (x, y, z, 1)	Allows translation to be expressed as matrix multiplication
Concatenation	Combining multiple transformations into one matrix	Efficient computation, single-step transformation, easier pipeline

Previous topic 55

Affine transformations: translation, rotation, scaling, shear

Next topic 57

Current transformation and matrix stacks

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COMP3145›Homogeneous coordinates and concatenation

HCI & Computer GraphicsTopic 56 of 73

Homogeneous coordinates and concatenation

5 minread

841words

Beginnerlevel

1. Homogeneous Coordinates

In 2D: a point $(x, y)$ is represented as $(x, y, 1)$
In 3D: a point $(x, y, z)$ is represented as $(x, y, z, 1)$

Why use homogeneous coordinates?

Translation can be represented as matrix multiplication, like other linear transformations
Allows concatenation of multiple transformations into a single matrix
Simplifies computations in graphics pipelines

Example (2D Translation): Point $P = (x, y)$ Translation by $(tx, ty)$ :

T = \begin{bmatrix} 1 & 0 & tx \ 0 & 1 & ty \ 0 & 0 & 1 \end{bmatrix}, \quad P' = T \cdot \begin{bmatrix} x \ y \ 1 \end{bmatrix} = \begin{bmatrix} x + tx \ y + ty \ 1 \end{bmatrix}

2. Concatenation of Transformations

Definition: Concatenation is the process of combining multiple transformations into a single matrix by matrix multiplication.

Key Points:

If you have multiple transformations (e.g., scale, then rotate, then translate), you can concatenate them:

M_{\text{final}} = T \cdot R \cdot S

Then apply the single matrix $M_{\text{final}}$ to the point:

P' = M_{\text{final}} \cdot P

This reduces computation and simplifies transformation pipelines.

Important:

Order matters! Matrix multiplication is not commutative.
- Example: Translate → Rotate ≠ Rotate → Translate

3. Example in 2D

Suppose we want to scale, rotate, and translate a point $P = (2, 3)$ :

Scale: $Sx = 2, Sy = 3$

S = \begin{bmatrix} 2 & 0 & 0 \ 0 & 3 & 0 \ 0 & 0 & 1 \end{bmatrix}

Rotate: θ = 90°

R = \begin{bmatrix} 0 & -1 & 0 \ 1 & 0 & 0 \ 0 & 0 & 1 \end{bmatrix}

Translate: $tx = 5, ty = 2$

T = \begin{bmatrix} 1 & 0 & 5 \ 0 & 1 & 2 \ 0 & 0 & 1 \end{bmatrix}

Concatenated Transformation:

M_{\text{final}} = T \cdot R \cdot S

Apply to point:

P' = M_{\text{final}} \cdot \begin{bmatrix} 2 \ 3 \ 1 \end{bmatrix}

This gives the final transformed position in one step.

4. Advantages of Homogeneous Coordinates and Concatenation

All transformations (translation, rotation, scaling, shear) are matrix multiplications.
Enables easy combination of multiple transformations.
Essential in graphics pipelines, animation, and modeling.
Facilitates 3D transformations and projections.

5. Summary

Concept	Definition	Benefit
Homogeneous coordinates	Extend points with an extra dimension (x, y, 1) or (x, y, z, 1)	Allows translation to be expressed as matrix multiplication
Concatenation	Combining multiple transformations into one matrix	Efficient computation, single-step transformation, easier pipeline

Previous topic 55

Affine transformations: translation, rotation, scaling, shear

Next topic 57

Current transformation and matrix stacks

Past Papers

Open this section to load past papers

Click on Show Past Papers to see past papers.