Transforming a Vector by a Matrix Part 4: Matrix Transformation Tutorial

Table of Contents

1. Introduction
2. Math Prerequisites

1. Importance of Correct Transformations
2. What a Matrix Represents
3. Matrix Multiplication
4. Transforming a Vector by a Matrix
5. Object Space Transformations
6. Camera Transformations
7. Inverse Transformations
8. Hierarchical Transformations

9. Precision
10. Conclusion

Transforming a Vector by a Matrix

This is the second operation which is required for our matrix transformations. It involves projecting a stationary vector onto transformed axis vectors using the dot product. One dot product is performed for each coordinate axis.

        x = x0 * matrix[0][0] + y0 * matrix[1][0] + z0 * matrix[2][0] +
            w0 * matrix[3][0];

        y = x0 * matrix[0][1] + y0 * matrix[1][1] + z0 * matrix[2][1] +
            w0 * matrix[3][1];

        z = x0 * matrix[0][2] + y0 * matrix[1][2] + z0 * matrix[2][2] +
            w0 * matrix[3][2];

The x0, y0, etc. coordinates are the original object space coordinates for the vector. That is, they never change due to transformation.

"Alright," you say. "Where did all the w coordinates come from???" Good question :) The w coordinates come from what is known as a homogenous coordinate system, which is basically a way to represent 3d space in terms of a 4d matrix. Because we are limiting ourselves to 3d, we pick a constant, nonzero value for w (1.0 is a good choice, since anything * 1.0 = itself). If we use this identity axiom, we can eliminate a multiply from each of the dot products:

        x = x0 * matrix[0][0] + y0 * matrix[1][0] + z0 * matrix[2][0] +
            matrix[3][0];

        y = x0 * matrix[0][1] + y0 * matrix[1][1] + z0 * matrix[2][1] +
            matrix[3][1];

        z = x0 * matrix[0][2] + y0 * matrix[1][2] + z0 * matrix[2][2] +
            matrix[3][2];