I have a 3D matrix of data in matlab, but I want to extract an arbitrarily rotated slice of data from that matrix and store it as a 2D matrix, which I can access. Similar to how the slice() function displays data sliced at any angle, except I would also like to be able to view and modify the data as if it were an array.
I have the coordinates of the pivot-point of the plane as well as the angles of rotation (in x, y and z axis), I have also calculated the equation of the plane in the form:
Ax + By + Cz = D
and can extract a 3D matrix containing only the data that fall on that plane, but I don't know how to then convert that into a simple 2D array.
Another way of doing it would be to somehow rotate the source matrix in the opposite direction of the angle of the plane, so as to line up the plane of data with the XY axis, and simply extract that portion of the matrix, but I do not know if rotating a matrix like that is possible.
I hope this hasn't been answered elsewhere, I've been googling it all day, but none of the problems seem to exactly match mine.
Thanks
You can take a look at the code here. I think the function is similar to what you are trying to solve.
The function extracts an arbitrary plane from a volume given the size of the plane, the center point of the plane, and the plane normal, i.e. [A,B,C]. It also outputs the volumetric index and coordinate of each pixel on the plane.
Aha! May have just solved it myself.
To produce the plane equation I rotate a normal vector of (0,0,1) using rotation matrices and then find D. If I also rotate the following vectors:
(1,0,0) //step in the x direction of our 2D array
and
(0,1,0) //step in the y direction of our 2D array
I'll have the gradients that denote how much my coordinates in x,y,z have to change before I step to the next column in my array, or to the next row.
I'll mock this up ASAP and mark it as the answer if it works
EDIT: Ok slight alteration, when I'm rotating my vectors I should also rotate the point in 3D space that represents the xyz coordinates of x=0,y=0,z=0 (although I'm rotating around the centre of the structure, so it's actually -sizex/2,-sizey/2,-sizez/2, where size is the size of the data, and then I simply add size/2 to each coordinate after the rotations to translate it back to where it should be).
Now that I have the gradient change in 3D as I increase the x coordinate of my 2D array and the gradient change as I increase the y coordinate, I can simply loop through all possible x and y coordinates (the resulting array will be 50x50 for a 50x50x50 array, I'm not sure what it will be for irregular sizes, which I'll need to work out eventually) in my 2D array and calculate the resulting 3D coordinates on my plane in the data. My rotated corner value serves as the starting point. Hooray!
Just got to work out a good test for this encompassing all angles and then I'll approve this as an answer
Related
I have made 3D analysis code and I want to split or crop 3D mesh into 2 parts with 2D plane, what i expected: the final result I need is to find out what are the nodes on the left side and the right side, what you see on the image below is the nodes of the 3D object (bumpy), Do you know what method or library I need to use to solve this problem?
my problem
Here is my data structure from the 2D plane:
Column 1: Face
Column 2: X coordinate
Column 3: Y coordinate
column 4: Z coordinate
Column 5: Finite Element Value
data structure
.
The data structure from the 3D mesh is containing the same data as the table above, Thanks so much!
We can know the plane XYZ Coordinates, so I tried to find by using <= to find the axis value is larger or smaller than the plane coordinates:
find x,y,z 3D model coordinate is smaller than x,y,z cut plane coordinate
[r] = find((Name_OT(:,1)>=x) & (Name_OT(:,2)>=y) & Name_OT(:,3)>=z);
the blue line is the plane, and the coloured one is the result from my code, the ideal result is the coloured nodes full, but what happened here the colour node has a big hole or gap
not good result
You need to first decide whether you want to segment your data by a (linear) plane or not. If you prefer to keep a curved surface, please follow my earlier comment and edit your question.
To get a plane fit to the data for your cut, you can use fit.
Base on the plane, you can get a normal vector of the plane. That is reading coefficients of fit results and is in the documentation. Using that normal vector, you can rotate all your data so that the plane is normal to z axis. The rotation is matrix multiplication. From there, you can use logical indexing to segment your data set.
You can ofc also get the normal component of the data points relative to the plane cut and decide on a direction that way. You still need fit. From that point, it's basic matrix manipulation. An nx1 vector can multiply a 1xn vector in Matlab. So projectors can also be constructed from basic matrix manipulation. At a glance, this method is computationally inefficient.
I have a set of data coordinates in 3D with respect to an Origin, say O1, and another set of data that represents the same movements but in 2D with respect to an Origin, say O2. What can I do to calculate the transformations that are required to implement on the 3D set to be able to compare the data points(2D vs 3D) in a 2D frame?
Setup of data generation
If the measurements are at the same times, then the following might work. I have not actually tried it and make no claim that it is the best solution, but it seems doable. Basically, after adjusting the coordinates so that they have the same origin, you want to minimize the following 2-norm:
|| vec(A*x) - vec(P*B*y) ||
where x is the 2xN array of the 2-D locations, A is a 2x2 rotation matrix that rotates x by the unknown angle theta, P is a 2x3 projection matrix that projects a 3x1 vector onto its first two dimensions (viz., just throws out the third value), B is a 3x3 rotation matrix, and y is the 3xN array of the 3-D locations.
The unknowns in the above are theta in the 2-D rotation matrix, and the three rotation angles needed to define B. The four angles that minimize the above should (at least I think) give you the rotation matrices needed to align the coordinate systems.
How is this idea applied to a polygon model in order to rotate the entire object? Can this be connected to circles with a formula for performing rotation?
You probably work with matrices (if you don't, I'd suggest you do), so what you want to check out is rotation matrices.
Basically, by multiplying the vectors by the rotation matrix, you obtain the rotated vector, and by applying this to every point of the polygon, you get your rotated polygon.
Note that it also applies to 3D rotations, even if it's not exactly the same matrix.
I have given 3d points of a scene or a subset of these points comprising one object of the scene. I would like to create a depth image from these points, that is the pixel value in the image encodes the distance of the corresponding 3d point to the camera.
I have found the following similar question
http://www.mathworks.in/matlabcentral/newsreader/view_thread/319097
however the answers there do not help me, since I want to use MATLAB. To get the image values is not difficult (e.g. simply compute the distance of each 3d point to the camera's origin), however I do not know how to figure out the corresponding locations in the 2d image.
I could only imagine that you project all 3d points on a plane and bin their positions on the plane in discrete, well, rectangles on the plane. Then you could average the depth value for each bin.
I could however imagine that the result of such a procedure would be a very pixelated image, not being very smooth.
How would you go about this problem?
Assuming you've corrected for camera tilt (a simple matrix multiplication if you know the angle), you can probably just follow this example
X = data(:,1);
Y = data(:,1);
Z = data(:,1);
%// This bit requires you to make some choices like the start X and Z, end X and Z and resolution (X and Z) of your desired depth map
[Xi, Zi] = meshgrid(X_start:X_res:X_end, Z_start:Z_res:Z_end);
depth_map = griddata(X,Z,Y,Xi,Zi)
I have a set of data in 3D which are in the same plane. I have a Triangle containing those data points in the same plane. But the Area of the Triangle is much larger. I want to find the smallest area triangle (co-ordinate of its 3 points) containing all the data point inside it. There are some concepts available for 2D data points, but I need to find this in 3D dimension.
It looks like Matlab has a function for this, convhull. You want to find the convex hull of the data set. http://www.mathworks.com/help/matlab/ref/convhull.html This function works for points in 2d or 3d space.