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I am not sure if a question like this was asked before but i searched and didn't found what i am looking for.
I know how to determine if a point is to the left or right of a 2D line. but suppose we have a vector in 3D. of course a 3D vector passes through infinite planes, but suppose we chose one plane of them in which we are interested, and we have a specific point on this plane which we want to know if it lies to the left or right or on our vector (with respect to the chosen plane). how to do this ?
You should explicitly define orientation of that plane - for example, define main (forward) normal N - like OZ axis is normal for OXY plane.
If you have A,B,C triangle and claim that it is oriented counterclockwise, you can calculate forward plane normal as N = AB x BC
For points A, B, D in given plane calculate mixed product (vector product of AB and AD, then scalar product of result and N)
mp = (AB x AD) . dot. N
Sign of this value is positive, if vectors AB, AD, N form right-handed triplet and D lies left to AB direction
An intuitive solution is to define a coordinate system for the plane as follows. Let's normalize the 3d vector in your question and call the resulting unit vector v, and let x be a point on your plane, whose unit normal we will denote as n. You can now chose a coordinate system centered at x, that is made by the three 3*1 unit vectors v, n and b=v.crossProduct(n).
The idea is that if you express a point in this coordinate system, then if its b coordinate is negative, you can says that it is, say, on the left. So, if its b coordinate is positive, it will be on the right.
Obviously, if you have a point q expressed in this coordinates system, you can write its expression q_w in world coordinates using
q_w=R*q+x
where the rotation matrix R is the matrix whose columns are the unit axes of the plane coordinate system:
R=[v n b]
So, if you have a point Q in world coordinates, using the inverse of the relation above, you compute transpose(R)*(Q-x), and look at whether the b coordinate is positive or negative.
I Have the orthographic projection of a unit cube with one of its vertex at origin as shown above. I have the x,y (no z) co ordinates of the projections. I would like to compute the angle of rotation of the plane to get the second orthographic projection from the first one (maybe euler angles??)
Is there any other easy way to compute this?
UPDATE:
Could I use this rotation matrix to get a system of equations in cos, sin angles and the x,y and x',y' and solve them easily? Or is there any easier way to get the angles back? (Am I on the right direction to solve this? )
First method
Use this idea to generate equations:
a1, a2 and a3 are coordinates in the original system, x y are the coordinates you get from the end-result and z is a coordinate you don’t know. This generates 2 equations for every point of the cube. E.g for point 0 with coordinates (-1, -1, 1) these are:
Do this for the 4 front points of the cube and you get 8 equations. Now add the fact that this is a rotation matrix -> the determinant is 1 and you have 9 equations. Solve these with any of the usual algorithms for solving equation systems and you have the transformation matrix. Getting the axis and angle from that is easy via google: http://www.euclideanspace.com/maths/geometry/rotations/conversions/matrixToAngle/
Second method
Naming your points 0, 1, 2, 3 a, b, c, d respectively, you can get the z coordinates of the vectors between them (e.g. b-a) with this idea:
you will still have to sort out if b3-a3 is positive, though. One way to do that is to use the centermost point as b (calculate distance from the center for all points, use the one with the minimal distance). Then you know for sure that b3-a3 is positive (if z is positive towards you).
Now assume that a is (0,0,0) in your transformed space and you can calculate all the point positions by adding the appropriate vectors to that.
To get the rotation you use the fact that you know where b-a did point in your origin space (e.g. (1,0,0)). You get the rotation angle via dot product of b-a and (1,0,0) and the rotation axis via cross product between those vectors.
I have a set of points (with unknow coordinates) and the distance matrix. I need to find the coordinates of these points in order to plot them and show the solution of my algorithm.
I can set one of these points in the coordinate (0,0) to simpify, and find the others. Can anyone tell me if it's possible to find the coordinates of the other points, and if yes, how?
Thanks in advance!
EDIT
Forgot to say that I need the coordinates on x-y only
The answers based on angles are cumbersome to implement and can't be easily generalized to data in higher dimensions. A better approach is that mentioned in my and WimC's answers here: given the distance matrix D(i, j), define
M(i, j) = 0.5*(D(1, j)^2 + D(i, 1)^2 - D(i, j)^2)
which should be a positive semi-definite matrix with rank equal to the minimal Euclidean dimension k in which the points can be embedded. The coordinates of the points can then be obtained from the k eigenvectors v(i) of M corresponding to non-zero eigenvalues q(i): place the vectors sqrt(q(i))*v(i) as columns in an n x k matrix X; then each row of X is a point. In other words, sqrt(q(i))*v(i) gives the ith component of all of the points.
The eigenvalues and eigenvectors of a matrix can be obtained easily in most programming languages (e.g., using GSL in C/C++, using the built-in function eig in Matlab, using Numpy in Python, etc.)
Note that this particular method always places the first point at the origin, but any rotation, reflection, or translation of the points will also satisfy the original distance matrix.
Step 1, arbitrarily assign one point P1 as (0,0).
Step 2, arbitrarily assign one point P2 along the positive x axis. (0, Dp1p2)
Step 3, find a point P3 such that
Dp1p2 ~= Dp1p3+Dp2p3
Dp1p3 ~= Dp1p2+Dp2p3
Dp2p3 ~= Dp1p3+Dp1p2
and set that point in the "positive" y domain (if it meets any of these criteria, the point should be placed on the P1P2 axis).
Use the cosine law to determine the distance:
cos (A) = (Dp1p2^2 + Dp1p3^2 - Dp2p3^2)/(2*Dp1p2* Dp1p3)
P3 = (Dp1p3 * cos (A), Dp1p3 * sin(A))
You have now successfully built an orthonormal space and placed three points in that space.
Step 4: To determine all the other points, repeat step 3, to give you a tentative y coordinate.
(Xn, Yn).
Compare the distance {(Xn, Yn), (X3, Y3)} to Dp3pn in your matrix. If it is identical, you have successfully identified the coordinate for point n. Otherwise, the point n is at (Xn, -Yn).
Note there is an alternative to step 4, but it is too much math for a Saturday afternoon
If for points p, q, and r you have pq, qr, and rp in your matrix, you have a triangle.
Wherever you have a triangle in your matrix you can compute one of two solutions for that triangle (independent of a euclidean transform of the triangle on the plane). That is, for each triangle you compute, it's mirror image is also a triangle that satisfies the distance constraints on p, q, and r. The fact that there are two solutions even for a triangle leads to the chirality problem: You have to choose the chirality (orientation) of each triangle, and not all choices may lead to a feasible solution to the problem.
Nevertheless, I have some suggestions. If the number entries is small, consider using simulated annealing. You could incorporate chirality into the annealing step. This will be slow for large systems, and it may not converge to a perfect solution, but for some problems it's the best you and do.
The second suggestion will not give you a perfect solution, but it will distribute the error: the method of least squares. In your case the objective function will be the error between the distances in your matrix, and actual distances between your points.
This is a math problem. To derive coordinate matrix X only given by its distance matrix.
However there is an efficient solution to this -- Multidimensional Scaling, that do some linear algebra. Simply put, it requires a pairwise Euclidean distance matrix D, and the output is the estimated coordinate Y (perhaps rotated), which is a proximation to X. For programming reason, just use SciKit.manifold.MDS in Python.
The "eigenvector" method given by the favourite replies above is very general and automatically outputs a set of coordinates as the OP requested, however I noticed that that algorithm does not even ask for a desired orientation (rotation angle) for the frame of the output points, the algorithm chooses that orientation all by itself!
People who use it might want to know at what angle the frame will be tipped before hand so I found an equation which gives the answer for the case of up to three input points, however I have not had time to generalize it to n-points and hope someone will do that and add it to this discussion. Here are the three angles the output sides will form with the x-axis as a function of the input side lengths:
angle side a = arcsin(sqrt(((c+b+a)*(c+b-a)*(c-b+a)*(-c+b+a)*(c^2-b^2)^2)/(a^4*((c^2+b^2-a^2)^2+(c^2-b^2)^2))))*180/Pi/2
angle side b = arcsin(sqrt(((c+b+a)*(c+b-a)*(c-b+a)*(-c+b+a)*(c^2+b^2-a^2)^2)/(4*b^4*((c^2+b^2-a^2)^2+(c^2-b^2)^2))))*180/Pi/2
angle side c = arcsin(sqrt(((c+b+a)*(c+b-a)*(c-b+a)*(-c+b+a)*(c^2+b^2-a^2)^2)/(4*c^4*((c^2+b^2-a^2)^2+(c^2-b^2)^2))))*180/Pi/2
Those equations also lead directly to a solution to the OP's problem of finding the coordinates for each point because: the side lengths are already given from the OP as the input, and my equations give the slope of each side versus the x-axis of the solution, thus revealing the vector for each side of the polygon answer, and summing those sides through vector addition up to a desired vertex will produce the coordinate of that vertex. So if anyone can extend my angle equations to handling beyond three input lengths (but I note: that might be impossible?), it might be a very fast way to the general solution of the OP's question, since slow parts of the algorithms that people gave above like "least square fitting" or "matrix equation solving" might be avoidable.
I'm doing something where I have a plane in a coord sys A with a set of points already on it. I also have a normal vector in space N. How can I rotate the points on coord sys A so that the underlying plane will have the same normal direction as N?
Wondering if any one has a good idea on how to do this. Thanks
If you have, or can easily compute, the normal vector to the plane that your points are currently in, I think the easiest way to do this will be to rotate around the axis common to the two planes. Here's how I'd go about it:
Let M be the vector normal to your current plane, and N be the vector normal to the plane you want to rotate into. If M == N you can stop now and leave the original points unchanged.
Calculate the rotation angle as
costheta = dot(M,N)/(norm(M)*norm(N))
Calculate the rotation axis as
axis = unitcross(M, N)
where unitcross is a function that performs the cross product and normalizes it to a unit vector, i.e. unitcross(a, b) = cross(a, b) / norm(cross(a, b)). As user1318499 pointed out in a comment, this step can cause an error if M == N, unless your implementation of unitcross returns (0,0,0) when a == b.
Compute the rotation matrix from the axis and angle as
c = costheta
s = sqrt(1-c*c)
C = 1-c
rmat = matrix([ x*x*C+c x*y*C-z*s x*z*C+y*s ],
[ y*x*C+z*s y*y*C+c y*z*C-x*s ]
[ z*x*C-y*s z*y*C+x*s z*z*C+c ])
where x, y, and z are the components of axis. This formula is described on Wikipedia.
For each point, compute its corresponding point on the new plane as
newpoint = dot(rmat, point)
where the function dot performs matrix multiplication.
This is not unique, of course; as mentioned in peterk's answer, there are an infinite number of possible rotations you could make that would transform the plane normal to M into the plane normal to N. This corresponds to the fact that, after you take the steps described above, you can then rotate the plane around N, and your points will be in different places while staying in the same plane. (In other words, each rotation you can make that satisfies your conditions corresponds to doing the procedure described above followed by another rotation around N.) But if you don't care where in the plane your points wind up, I think this rotation around the common axis is the simplest way to just get the points into the plane you want them in.
If you don't have M, but you do have the coordinates of the points in your starting plane relative to an origin in that plane, you can compute the starting normal vector from two points' positions x1 and x2 as
M = cross(x1, x2)
(you can also use unitcross here but it doesn't make any difference). If you have the points' coordinates relative to an origin that is not in the plane, you can still do it, but you'll need three points' positions:
M = cross(x3-x1, x3-x2)
A single vector (your normal - N) will not be enough. You will need another two vectors for the other two dimensions. (Imagine that your 3D space could still rotate/spin around the normal vector, and you need another 2 vectors to nail it down). Once you have the normal and another one on the plane, the 3rd one should be easy to find (left- or right-handed depending on your system).
Make sure all three are normalized (length of 1) and put them in a matrix; use that matrix to transform any point in your 3D space (use matrix multiplication). This should give you the new coordinates.
I'm thinking make a unit vector [0,0,1] and use the dot-product along two planes to find the angle of difference, and shift all your points by those angles. This is assuming you want the z-axis to align with the normal vector, else just use [1,0,0] or [0,1,0] for x and y respectively.
I'm looking for an algorithm to find the common intersection points between 3 spheres.
Baring a complete algorithm, a thorough/detailed description of the math would be greatly helpful.
This is the only helpful resource I have found so far:
http://mathforum.org/library/drmath/view/63138.html
But neither method described there is detailed enough for me to write an algorithm on.
I would prefer the purely algebraic method described in the second post, but what ever works.
Here is an answer in Python I just ported from the Wikipedia article. There is no need for an algorithm; there is a closed form solution.
import numpy
from numpy import sqrt, dot, cross
from numpy.linalg import norm
# Find the intersection of three spheres
# P1,P2,P3 are the centers, r1,r2,r3 are the radii
# Implementaton based on Wikipedia Trilateration article.
def trilaterate(P1,P2,P3,r1,r2,r3):
temp1 = P2-P1
e_x = temp1/norm(temp1)
temp2 = P3-P1
i = dot(e_x,temp2)
temp3 = temp2 - i*e_x
e_y = temp3/norm(temp3)
e_z = cross(e_x,e_y)
d = norm(P2-P1)
j = dot(e_y,temp2)
x = (r1*r1 - r2*r2 + d*d) / (2*d)
y = (r1*r1 - r3*r3 -2*i*x + i*i + j*j) / (2*j)
temp4 = r1*r1 - x*x - y*y
if temp4<0:
raise Exception("The three spheres do not intersect!");
z = sqrt(temp4)
p_12_a = P1 + x*e_x + y*e_y + z*e_z
p_12_b = P1 + x*e_x + y*e_y - z*e_z
return p_12_a,p_12_b
Probably easier than constructing 3D circles, because working mainly on lines and planes:
For each pair of spheres, get the equation of the plane containing their intersection circle, by subtracting the spheres equations (each of the form X^2+Y^2+Z^2+aX+bY+c*Z+d=0). Then you will have three planes P12 P23 P31.
These planes have a common line L, perpendicular to the plane Q by the three centers of the spheres. The two points you are looking for are on this line. The middle of the points is the intersection H between L and Q.
To implement this:
compute the equations of P12 P23 P32 (difference of sphere equations)
compute the equation of Q (solve a linear system, or compute a cross product)
compute the coordinates of point H intersection of these four planes. (solve a linear system)
get the normal vector U to Q from its equation (normalize a vector)
compute the distance t between H and a solution X: t^2=R1^2-HC1^2, (C1,R1) are center and radius of the first sphere.
solutions are H+tU and H-tU
A Cabri 3D construction showing the various planes and line L
UPDATE
An implementation of this answer in python complete with an example of usage can be found at this github repo.
It turns out the analytic solution is actually quite nice using this method and can tell you when a solution exists and when it doesn't (it is also possible to have exactly one solution.) There is no reason to use Newton's method.
IMHO, this is far easier to understand and simpler than trilateration given below. However, both techniques give correct answers in my testing.
ORIGINAL ANSWER
Consider the intersection of two spheres. To visualize it, consider the 3D line segment N connecting the two centers of the spheres. Consider this cross section
(source: googlepages.com)
where the red-line is the cross section of the plane with normal N. By symmetry, you can rotate this cross-section from any angle, and the red line segments length can not change. This means that the resulting curve of the intersection of two spheres is a circle, and must lie in a plane with normal N.
That being said, lets get onto finding the intersection. First, we want to describe the resulting circle of the intersection of two spheres. You can not do this with 1 equation, a circle in 3D is essentially a curve in 3D and you cannot describe curves in 3D by 1 eq.
Consider the picture
(source: googlepages.com)
let P be the point of intersection of the blue and red line. Let h be the length of the line segment along the red line from point P upwards. Let the distance between the two centers be denoted by d. Let x be the distance from the small circle center to P. Then we must have
x^2 +h^2 = r1^2
(d-x)^2 +h^2 = r2^2
==> h = sqrt(r1^2 - 1/d^2*(r1^2-r2^2+d^2)^2)
i.e. you can solve for h, which is the radius of the circle of intersection. You can find the center point C of the circle from x, along the line N that joins the 2 circle centers.
Then you can fully describe the circle as (X,C,U,V are all vector)
X = C + (h * cos t) U + (h * sin t) V for t in [0,2*PI)
where U and V are perpendicular vectors that lie in a plane with normal N.
The last part is the easiest. It remains only to find the intersection of this circle with the final sphere. This is simply a plug and chug of the equations (plug in for x,y,z in the last equation the parametric forms of x,y,z for the circle in terms of t and solve for t.)
edit ---
The equation that you will get is actually quite ugly, you will have a whole bunch of sine's and cosine's equal to something. To solve this you can do it 2 ways:
write the cosine's and sine's in terms of exponentials using the equality
e^(it) = cos t + i sin t
then group all the e^(it) terms and you should get a quadratic equations of e^(it)'s
that you can solve for using the quadratic formula, then solve for t. This will give you the exact solution. This method will actually tell you exactly if a solution exists, two exist or one exist depending on how many of the points from the quadratic method are real.
use newton's method to solve for t, this method is not exact but its computationally much easier to understand, and it will work very well for this case.
Basically you need to do this in 3 steps. Let's say you've got three spheres, S1, S2, and S3.
C12 is the circle created by the intersection of S1 and S2.
C23 is the circle created by the intersection of S2 and S3.
P1, P2, are the intersection points of C12 and C13.
The only really hard part in here is the sphere intersection, and thankfully Mathworld has that solved pretty well. In fact, Mathworld also has the solution to the circle intersections.
From this information you should be able to create an algorithm.
after searching the web this is one of the first hits, so i am posting the most clean and easy solution i found after some hours of research here: Trilateration
This wiki site contains a full description of a fast and easy to understand vector approach, so one can code it with little effort.
Here is another interpretation of the picture which Eric posted above:
Let H be the plane spanned by the centers of the three spheres. Let C1,C2,C3 be the intersections of the spheres with H, then C1,C2,C3 are circles. Let Lij be the line connecting the two intersection points of Ci and Cj, then the three lines L12,L23,L13 intersect at one point P. Let M be the line orthogonal to H through P, then your two points of intersection lie on the line M; hence you just need to intersect M with either of the spheres.