I have a series of points in two 3D systems. With them, I use np.linalg.lstsq to calculate the affine transformation matrix (4x4) between both. However, due to my project, I have to "disable" the shear in the transform. Is there a way to decompose the matrix into the base transformations? I have found out how to do so for Translation and Scaling but I don't know how to separate Rotation and Shear.
If not, is there a way to calculate a transformation matrix from the points that doesn't include shear?
I can only use numpy or tensorflow to solve this problem btw.
I'm not sure I understand what you're asking.
Anyway If you have two sets of 3D points P and Q, you can use Kabsch algorithm to find out a rotation matrix R and a translation vector T such that the sum of square distances between (RP+T) and Q is minimized.
You can of course combine R and T into a 4x4 matrix (of rotation and translation only. without shear or scale).
Related
I need to render a 2d gaussian and still be able to differentiate with respect to the 2d mean, which has type float. The standard deviation of the gaussian can be constant. Same for the size of the matrix that is generated.
Any idea how to do this in tensorflow?
CLARIFICATION:
I need a function draw2dGaussian(mean2d) which returns a 2d matrix M. The matrix M will show a discretized 2d gaussian centered at the location mean2d. Note that mean2d is a pair of 2 floats. The matrix M will be 0 at the points far enough from the mean2d.
The requirement of this function draw2dGaussian is that it has to be differentiable with respect to mean2d.
I think openDR http://files.is.tue.mpg.de/black/papers/OpenDR.pdf might be able to offer such a function, but I was wondering if somebody had a simpler solution.
You are looking for the reparametrization trick. For a one-dimensional gaussian, N(mean, var) can be written as mean + sqrt(var) * N(0, 1). A similar construction applies to 2d gaussians but with a covariance matrix instead of a constant variance.
I understand that to create a shape (let's say a 3D sphere for an example) that I have to first find the vertex locations of the shape and second, use the parametric equation in order to create the x, y, z points of the triangle meshes. I am currently looking at a sample code to create shapes and it appears that after using the parametric equation in order to find the vectors of the triangle meshes, unit normals to the sphere at the vertices are found.
I understand why regular vectors in the first step are used to create the 3D shape and that a normal vector is perpendicular to the shape object, but I don't understand why the unit normal vectors at the vertices are used to create the shapes? What's the purpose of finding the normal of the vectors?
I am not sure I totally understand your question, but one very important use for normals in computer graphics is calculating reflections. For instance, if you're writing a simple raytracer, Lambertian reflectance is quite easy to compute if you know the normal vector where your camera ray intersects a surface. Normals are similarly required for (off the top of my head) the majority of calculations involved in more complex rendering techniques.
What i am trying to do http://www.youtube.com/watch?v=CaTI2d0tQME 3:15
In my 3D api there is quad.rotation[x,y,z], quad[x,y,z] which is center of it and width/height. I understand that vertices are being calculated from all of the given. And normal can be calculated from vertices but i have a feeling i should be able to get it just from the rotation?
Yes you can !
Your quad must be axis-oriented (along the X, Y or Z axis, which is its normal vector in its local space). Compose this vector with the quad rotation matrix and you will have your new, nice and shiny normal vector in world space !
A little warning : if the quad transformation matrix is generated by any 3D engine, it could contain scaling factors that will mess the normal vector up. In this case, the classical solution is to compute the transposed inverse of the matrix, or to generate your custom transformation matrix with the quad's rotation values.
Given a general planar 3D polygon, is there a general way to find the orthonormal basis for that planar polygon?
The most straight forward way to do it is to assume to take the first 3 points of the polygon, and form two vectors each, and these are the two orthonormal basis vectors that we are looking for. But the problem for this approach is that these 3 points may line on the same line in the polygon, and hence instead of getting two orthonormal vectors, we get only one.
Another approach to find the second orthonormal vector is to loop through the polygon and find another point that forms a different orthonormal vector than the first one, but this approach is susceptible to numerical errors (e.g, what if the second vector is almost the same with the first vector? The numerical errors can be significant).
Is there any other better approach?
You can use the cross product of any two lines connected by any two vertices. If the cross product is too low then you're in degenerate territory.
You can also take the centroid (the avg of the points, which is guaranteed to lie on the same plane) and do pick the largest of any two cross products of the vectors from the centroid to any vertex. This will be the most accurate normal. Please note that if the largest cross product is small, you may have an inaccurate normal.
If you can't find any cross product that isn't close to 0, your original poly is degenerate and a normal will be hard to find. You could use arbitrary precision or adaptive precision algebra in this case, but, of course, the round-off error is already significant in the source data, so this may not help. If possible, remove degenerate polys first, and if you have to, sew the mesh back up :).
It's a bit ott but one way would be to compute the covariance matrix of the points, and then diagonalise that. If the points are indeed planar then one of the eigenvalues of the covariance matrix will be zero (or rather very small, due to finite precision arithmetic) and the corresponding eigenvector will be a normal to the plane; the other two eigenvectors will span the plane of the polygon.
If you have N points, and the i'th coordinate of the k'th point is p[k,i], then the mean (vector) and (3x3) covariance matrix can be computed by
m[i] = Sum{ k | p[k,i]}/N (i=1..3)
C[i,j] = Sum{ k | (p[k,i]-m[i])*(p[k,j]-m[j]) }/N (i,j=1..3)
Note that C is symmetric, so that to find how to diagonalise it you might want to look up the "symmetric eigenvalue problem"
I have a class that holds a 4x4 matrix for scaling and translations. How would I implement rotation methods to this class? And should I implement the rotation as a separate matrix?
You can multiply Your current matrix with a rotation matrix. Take a look at http://en.wikipedia.org/wiki/Rotation_matrix
There's a site which I use every time when I need to look up the details of a 3D transformation, called http://www.euclideanspace.com. The particular page on matrix rotations can be found here.
Edit: Rotation around a given axis, look at the axis & angle representation. This page also links to a description on how to translate one representation to another.
If you need to rotate around mutiple axes, simply multiply the corresponding matrices.
Answering the second half of the question, a single 4x4 matrix is perfectly capable of holding a scaling, a translation, and a rotation. So unless you've put special limitations on what sort of 4x4 matrices you can handle, a single 4x4 is a fine for what you want.
As for rotation about an arbitrary vector (as you are asking in comments), look at the "Rotation about an arbitrary vector" section in the Wikipedia article yabcok links to. You will want to extend that to a 4x4 matrix by padding it out with zeros except for the 4,4 (scaling) position, which should be one. Then use matrix multiplication with your scaling/translation 4x4 to generate a new 4x4 matrix.
You want to make sure you find a reference which talks about the right kind of matrix that's used for computer graphics (namely 3D homogeneous coordinates using a 4x4 transformation matrix for rotation/translation/skewing).
See a computer graphics "bible" such as Foley and Van Dam (pg. 213), or one of these:
The Mathematics of the 3D Rotation Matrix
Mathematics of 3D Graphics
MSDN 3D graphics tutorial
SIGGRAPH article about 3D rotation
other page from CProgramming.com
This page has quite a bit of useful information:
http://knol.google.com/k/matrices-for-3d-applications-translation-rotation