Question

I’m currently building my own quaternion camera. As far as I know, you only need one quaternion to fully specify the camera’s orientation (correct me if I’m wrong). So, how would I go about creating the view matrix? I’m using C++ as a programming language by the way.

Any help would be appreciated.

Answer 1

First some caveats. You’ll see lots of apparently conflicting formulae on the ‘net and in literature on this subject. Most of the conflicts are apparent only. A few are real conflicts, but that’s because somebody got the math wrong. The problem is that there is no single right way to do it. You need to know how you are using quaternions and matrices, how the source is using them, and how to rectify those apparent discrepancies.

Rotation versus transformation
Your camera has a reference frame associated with it, as does the underlying space. Does your matrix represent the physical rotation of the camera from the underlying space to the camera’s orientation or the matrix that transforms vectors as represented in the underlying space to the frame of the camera? (Or something else; there are four choices here.) These choices are related; the transformation matrix is the transpose of the rotation matrix. Transformation and rotation are conjugate operations. The same concept applies to quaternions. Are you using transformation quaternions or rotation quaternions? These are once again related concepts; one is the conjugate of the other.

Left versus right quaternions
Given a unit quaternion q to transform or rotate a vector v, some use qvq^* to transform/rotate the vector, others use q^*vq. Which form is correct? Both are. The two forms differ only in whether the unconjugated quaternion is to the left (qvq^*) or to the right (q^*vq) of the vector to be transformed/rotated.

Column versus row vectors
Most people use column vectors, but some do use row vectors. Here you run into a left versus right issue with matrices. Column vectors transform/rotate via Mv, with the matrix to the left of the vectors; row vectors via vM, with the matrix on the right.

Impact
You have to be careful in reading the literature. With regard to forming a matrix from a quaternion you need to watch out for sign changes in constructing the off-diagonal elements of the matrix. One formulation’s addition/subtraction may change to subtraction/addition in another formulation.

Left transformation quaternions to row vector transformation matrices
I use left transformation quaternions and transformation matrices, and I represent vectors as row vectors. I also represent a quaternion q as comprising a real scalar part q_s and a vectorial imaginary part q_v. Given these representations, the computations to generate a matrix from a quaternion are (pseudocode):

// Compute the cosine of the rotation angle.
cost = 2.0*qs*qs - 1.0;

// Construct the diagonal of the matrix:
// T_ii = cost + 2qv_i^2
for (i = 0; i < 3; ++i) {
   T[i][i] = cost + 2.0*qv[i]*qv[i];
}

// Construct off-diagonal transformation matrix elements:
//   T_ij = 2 (qv_i qv_j - eps_ijk qs qv_k), where eps is the Levi-Civita symbol
for (k = 0; k < 3; ++k) {
   i = (k+1)%3;
   j = (i+1)%3;
   T[i][j] = 2.0*(qv[i]*qv[j] - qs*qv[k]);
   T[j][i] = 2.0*(qv[i]*qv[j] + qs*qv[k]);
}

You might want to expand those loops out. The first loop expands into three statements, the latter, six. You don’t need to compute i and j in the expansion of the latter loop; the expansion of the loop makes them fixed quantities.

Alternate representations
Those caveats above aren’t as bad as they seem. You need to make sure my representation is consistent with yours. Odds are 50-50 that it isn’t. If it isn’t, simply swap the assignments to the off-diagonal elements. Use the computation of T[i][j] for T[j][i], and vice versa. How to tell:

• Start with s=1.
• Multiply s by -1 if you use rotation quaternions instead of transformation quaternions.
• Multiply s by -1 if you use right quaternions instead of left quaternions.
• Multiply s by -1 if you use rotation matrices instead of transformation matrices.
• Multiply s by -1 if you use row vectors instead of column vectors.

Use my formulation if the final value of s is 1. If it’s -1, simply swap the assignments to T[i][j] and T[j][i]. Or you could change the addition to subtraction, the subtraction to addition.

One last gotcha
The above calculation applies when the scalar part is not close to zero. It would be valid everywhere if we had infinite precision arithmetic. You might want to use a separate calculation for rotations that are very close to zero or 180 degrees.