The correct way to interpolate between two points on a sphere is using slerp.
How would one interpolate between more than two points on a sphere? So summing a set of points with different weights on the surface of a sphere?
Simply summing the points multiplied by their weights and then normalising the result is not accurate enough when the angles are large. We need 'true' spherical interpolation.
I asked this question on math.stackexchange.com, and someone found a paper that describes exactly this. Here it is: Spherical Averages and Applications to Spherical Splines and Interpolation
The problem I see is:
Slerp gives constant velocity. That is, a given increment in your interpolation parameter gives you the same distance on the sphere, regardless of where you are on the [0,1] range.
Unfortunately, because the sphere is curved, you can't do this for more than one interpolation parameter. Either you need to give up constant velocity, or give up interpolating with more than one parameter.
You may be able to find an interpolation function that isn't constant velocity that nonetheless satisfies your requirements. But because of the above problem, I don't think it will correspond directly and symmetrically to the 1-D slerp.
Related
How does the SVM algorithm find the optimum hyperplane? The positive margin hyperplane equation is w.x-b=1, the negative margin hyperplane equation is w.x-b=-1, and the middle(optimum) hyperplane equation is w.x-b=0).
I understand how a hyperplane equation can be got by using a normal vector of that plane and a known vector point(not the whole vector) by this tutorial. Lets say the known vector point is x1, the whole vector will be (x-x1), for some x. If w is the normal vector of the plane, then w.(x-x1)=0; eventually we will get the form w.x=b
Now, for getting a hyperplane, we need a normal vector and known point. How does the algorithm create a hyperplane at the middle where there is no data point (which I think is a known vector point needed in the equation) from training data?
Maybe I misunderstand something or my logic is not correct.
You misunderstand one basic fact: the algorithm is not required to represent a hyperplane in terms of w.x-b = 0, using a given data point. The algorithm is free to change this into any form convenient to each of its functions.
The solution is obvious, as you've already found it: the algorithm does not have to use one of the points form the data set. In fact, if the partition is ideal (no data on the wrong side), there is no point in the middle.
However, finding that hyperplane is trivial. (1) The positive and negative hyperplanes are parallel, and (2) the optimum plane bisects their separation. By (1), all three planes have the same normal vector. By (2), the reference point can be the midpoint of any segment connecting two points on opposite planes.
Briefly, pick a positive support vector and a negative support vector; these lie one on each of the planes. Find the midpoint between them, convolve with the normal vector, and there's your optimum plane.
I struggle with the orientation of an object I am moving along a hermite curve.
I figured out how to move it at constant speed at also have the tangent of my curve, which would be the forward vector of the moving object. My problem is: How do I know the up and right vector? The easiest way would be to start at a given rotation and then step through the curve always taking the last rotation as a reference for the next one, like in this reference:
Camera movement along a splinepdf
But this would result in an uncontrollable rotation at the end of the spline. What I am trying to do is to create an algorithm which gives you the correct orientation at any point of the curve, without stepping through it. Ideally it would use the orientation of the two controlpoints for the current segment as a reference.
I thought of using some kind of pre-calculated data, which is created from the two orientations of the controlpoints and the current curve segments form, but didn't manage to come up with a solution.
I would be happy to get any answers or just ideas how to approach this problem.
Let C(t) be the camera trajectory, with tangent vector T(t). The tangent vector controls the pitch and the yaw. What you are missing is roll control.
Define an auxiliary trajectory D(t) that "parallels" C(t) and use the vector CD(t). The up vector is given by U(t)=T(t) /\ CD(t) (normalized), and the right vector by U(t) /\ T(t) (normalized).
OK i came up with a solution using frenet frames. I define an orientation for each of my control points, then i calculate a number of points along the spline for each segment. Each points orientation is then calculated using the previous points orientation. The orientatin of the first point equals the orientation of the control point.
Here is a very nice description of the procedure.
After calculating each points orientation, you can interpolate them so the last points orientation matches the orientation of the next controlpoint.
I'm writing a data analysis program and part of it requires finding the volume of a shape. The shape information comes in the form of a lost of points, giving the radius and the angular coordinates of the point.
If the data points were uniformly distributed in coordinate space I would be able to perform the integral, but unfortunately the data points are basically randomly distributed.
My inefficient approach would be to find the nearest neighbours to each point and stitch the shape together like that, finding the volume of the stitched together parts.
Does anyone have a better approach to take?
Thanks.
IF those are surface points, one good way to do it would be to discretize the surface as triangles and convert the volume integral to a surface integral using Green's Theorem. Then you can use simple Gauss quadrature over the triangles.
Ok, here it is, along duffymo's lines I think.
First, triangulate the surface, and make sure you have consistent orientation of the triangles. Meaning that orientation of neighbouring triangle is such that the common edge is traversed in opposite directions.
Second, for each triangle ABC compute this expression: H*cross2D(B-A,C-A), where cross2D computes cross product using coordinates X and Y only, ignoring the Z coordinates, and H is the Z-coordinate of any convenient point in the triangle (although the barycentre would improve precision).
Third, sum up all the above expressions. The result would be the signed volume inside the surface (plus or minus depending on the choice of orientation).
Sounds like you want the convex hull of a point cloud. Fortunately, there are efficient ways of getting you there. Check out scipy.spatial.ConvexHull.
I'm working in OpenCV but I don't think there is a function for this. I can find a function for finding affine transformations, but affine transformations include scaling, and I only want to consider rotation + translation.
Imagine I have two sets of points in 2d - let's say each set has exactly 50 points.
E.g. set A = {x1, y1, x2, y2, ... , x50, y50}
set B = {x1', y1', x2', y2', ... , x50', y50'}
I want to find the rotation and translation combination that gets closest to mapping set A onto set B. I guess I would define "closest" as minimises the average distance between points in A and corresponding points in B. I.e., minimises the average distance between (x1, y1) and (x1', y1'), etc.
I guess I could use brute force testing all possible translations and rotations but this would be extremely inefficient. Does anyone know a simpler way?
Thanks!
This problem has a very elegant solution in terms of singular value decomposition of the proximity matrix (distances between pairs of points). The name of this is the orthogonal Procrustes problem, after the Greek legend about a fellow who offered travellers a bed that would fit anyone.
The solution comes from finding the nearest orthogonal matrix to a given (not necessarily orthogonal) matrix.
The way I would do it in Excel is to make a couple columns representing the points.
Cells representing rotation/translation of a set (no need to rotate and translate both of them).
Then columns representing those same points rotated/translated.
Then another column for the distance between the points of the rotated/translated points.
Then a cell of the sum of the distances between points.
Finally, use Solver to optimize the rotation and translation cells.
If you fix some rotation you can get an answer using ternary search. Run search in x and for every tested x run it in y to get the best value. This will give you the correct answer since the function (sum of corresponding distances) is convex (this can be proved through observing that restriction of the function to any line is a one-dimensional convex function; and the last is a standard fact: the sum of several convex functions is convex).
Instead of brute force over the angle I can propose such a method based on the ternary search. Choose some not very large step S. Compute the target function for every angle in (0, S, 2S,...). Then, if S is small enough, we can exclude some of segments (iS, (i + 1)S) from consideration. Namely ones with relatively large values of function with angles iS and (i + 1)S. Being implemented carefully this can give an answer and can do it faster than brute force.
I'm looking for a way to determine the optimal X/Y/Z rotation of a set of vertices for rendering (using the X/Y coordinates, ignoring Z) on a 2D canvas.
I've had a couple of ideas, one being pure brute-force involving performing a 3-dimensional loop ranging from 0..359 (either in steps of 1 or more, depending on results/speed requirements) on the set of vertices, measuring the difference between the min/max on both X/Y axis, storing the highest results/rotation pairs and using the most effective pair.
The second idea would be to determine the two points with the greatest distance between them in Euclidean distance, calculate the angle required to rotate the 'path' between these two points to lay along the X axis (again, we're ignoring the Z axis, so the depth within the result would not matter) and then repeating several times. The problem I can see with this is first by repeating it we may be overriding our previous rotation with a new rotation, and that the original/subsequent rotation may not neccesarily result in the greatest 2D area used. The second issue being if we use a single iteration, then the same problem occurs - the two points furthest apart may not have other poitns aligned along the same 'path', and as such we will probably not get an optimal rotation for a 2D project.
Using the second idea, perhaps using the first say 3 iterations, storing the required rotation angle, and averaging across the 3 would return a more accurate result, as it is taking into account not just a single rotation but the top 3 'pairs'.
Please, rip these ideas apart, give insight of your own. I'm intreaged to see what solutions you all may have, or algorithms unknown to me you may quote.
I would compute the principal axes of inertia, and take the axis vector v with highest corresponding moment. I would then rotate the vertices to align v with the z-axis. Let me know if you want more details about how to go about this.
Intuitively, this finds the axis about which it's hardest to rotate the points, ie, around which the vertices are the most "spread out".
Without a concrete definition of what you consider optimal, it's impossible to say how well this method performs. However, it has a few desirable properties:
If the vertices are coplanar, this method is optimal in that it will always align that plane with the x-y plane.
If the vertices are arranged into a rectangular box, the box's shortest dimension gets aligned to the z-axis.
EDIT: Here's more detailed information about how to implement this approach.
First, assign a mass to each vertex. I'll discuss options for how to do this below.
Next, compute the center of mass of your set of vertices. Then translate all of your vertices by -1 times the center of mass, so that the new center of mass is now (0,0,0).
Compute the moment of inertia tensor. This is a 3x3 matrix whose entries are given by formulas you can find on Wikipedia. The formulas depend only on the vertex positions and the masses you assigned them.
Now you need to diagonalize the inertia tensor. Since it is symmetric positive-definite, it is possible to do this by finding its eigenvectors and eigenvalues. Unfortunately, numerical algorithms for finding these tend to be complicated; the most direct approach requires finding the roots of a cubic polynomial. However finding the eigenvalues and eigenvectors of a matrix is an extremely common problem and any linear algebra package worth its salt will come with code that can do this for you (for example, the open-source linear algebra package Eigen has SelfAdjointEigenSolver.) You might also be able to find lighter-weight code specialized to the 3x3 case on the Internet.
You now have three eigenvectors and their corresponding eigenvalues. These eigenvalues will be positive. Take the eigenvector corresponding to the largest eigenvalue; this vector points in the direction of your new z-axis.
Now, about the choice of mass. The simplest thing to do is to give all vertices a mass of 1. If all you have is a cloud of points, this is probably a good solution.
You could also set each star's mass to be its real-world mass, if you have access to that data. If you do this, the z-axis you compute will also be the axis about which the star system is (most likely) rotating.
This answer is intended to be valid only for convex polyhedra.
In http://203.208.166.84/masudhasan/cgta_silhouette.pdf you can find
"In this paper, we study how to select view points of convex polyhedra such that the silhouette satisfies certain properties. Specifically, we give algorithms to find all projections of a convex polyhedron such that a given set of edges, faces and/or vertices appear on the silhouette."
The paper is an in-depth analysis of the properties and algorithms of polyhedra projections. But it is not easy to follow, I should admit.
With that algorithm at hand, your problem is combinatorics: select all sets of possible vertexes, check whether or not exist a projection for each set, and if it does exists, calculate the area of the convex hull of the silhouette.
You did not provide the approx number of vertex. But as always, a combinatorial solution is not recommended for unbounded (aka big) quantities.