I'm trying to make a 3D object do a wobble effect, very much like a boss in StarFox 64 did when it teleported (see this video at 5:17 for reference). This seems like either a skewing effect, or perhaps an un-uniform scale that rotated around and was applied without rotating the object itself.
Does anyone have any idea how this might be done, or perhaps does anyone have any links to programs where I can play with the matrices directly to see how this is done?
You can use skew based on roll axis in the Euler angles coordinate system
See Euler angles
http://en.wikipedia.org/wiki/Euler_angles
Euler angles-matrix transformation ("General rotations" part of the article):
http://en.wikipedia.org/wiki/Rotation_matrix
An euler angles-matrix conversion utility in DirectX SDK
http://msdn.microsoft.com/en-us/library/microsoft.windowsmobile.directx.matrix.rotationyawpitchroll%28v=VS.85%29.aspx
And threads about skew matrices
skew matrix algorithm
http://www.quantunet.com/flash8/knowledgebase/actionscript/advanced/matrix/matrix_skew.html
Related
I have a .xyz file that has irregularly spaced points and gives the position and surface normal (ie XYZIJK). Are there algorithms out there that can reconstruct the surface that factor in the IJK vectors? Most algorithms I have found assume that surface normals aren't known.
This would ultimately be used to plot surface error data (from the nominal surface) using python 3.x, and I'm sure I will have many more follow on questions once I find a good reconstruction algorithm.
The state of the art right now is Poisson Surface Reconstruction and its screened variant. Code for both is available, e.g. under http://www.cs.jhu.edu/~misha/Code/PoissonRecon/Version8.0/. It is also implemented in MeshLab if you want to take a quick look.
If you want to take a look at other methods, check out this STAR. Page three has a table of a couple of approaches and their inputs.
I'm experimenting some issues when work with real worlds.
The center of my camera is 280000, 45787254 (for example).
The extension of my world is about 500 x 500 (not too big)
I'm using data based in metric units (meters).
I have created a tile map structure build with simple planes.
I see little gaps between the plane borders and this planes are built to be contiguous (that is xmin of the adjacent plane is equal to xmax of previous).
In the past I have issues related with ray cast.
Matrix projection with this big units have low precision.
Change near value to number great than 10 can be the fix. However, using this value means bad visualization (you can't place the cam much near of the scene, it disappears).
I talked with the guy who develops potree and he said me is had to move the lidar worlds to 0,0 to work properly.
So... the final solution is to work in 0,0 worlds, isn't it ?
Or is there any trick we can do at matrix calculations?
I'd like to know three.js developers.
Floating point math is best at ranges close to zero, you just end up compounding errors as you move far away. You can always do as much math as possible near the origin and then translate the result to wherever you need, that will help with some of it, but if you can, work in local coordinates.
Potree probably gets odd ripple-looking aliasing effects when too far from the origin, no?
Assume I have a model that is simply a cube. (It is more complicated than a cube, but for the purposes of this discussion, we will simplify.)
So when I am in Sketchup, the cube is Xmm by Xmm by Xmm, where X is an integer. I then export the a Collada file and subsequently load that into threejs.
Now if I look at the geometry bounding box, the values are floats, not integers.
So now assume I am putting cubes next to each other with a small space in between say 1 pixel. Because screens can't draw half pixels, sometimes I see one pixel and sometimes I see two, which causes a lack of uniformity.
I think I can resolve this satisfactorily if I can somehow get the imported model to have integer dimensions. I have full access to all parts of the model starting with Sketchup, so any point in the process is fair game.
Is it possible?
Thanks.
Clarification: My app will have two views. The view that this is concerned with is using an OrthographicCamera that is looking straight down on the pieces, so this is really a 2D view. For purposes of this question, after importing the model, it should look like a grid of squares with uniform spacing in between.
UPDATE: I would ask that you please not respond unless you can provide an actual answer. If I need help finding a way to accomplish something, I will post a new question. For this question, I am only interested in knowing if it is possible to align an imported Collada model to full pixels and if so how. At this point, this is mostly to serve my curiosity and increase my knowledge of what is and isn't possible. Thank you community for your kind help.
Now you have to learn this thing about 3D programming: numbers don't mean anything :)
In the real world 1mm, 2.13cm and 100Kg specify something that can be measured and reproduced. But for a drawing library, those numbers don't mean anything.
In a drawing library, 3D points are always represented with 3 float values.You submit your points to the library, it transforms them in 2D points (they must be viewed on a 2D surface), and finally these 2D points are passed to a rasterizer which translates floating point values into integer values (the screen has a resolution of NxM pixels, both N and M being integers) and colors the actual pixels.
Your problem simply is not a problem. A cube of 1mm really means nothing, because if you are designing an astronomic application, that object will never be seen, but if it's a microscopic one, it will even be way larger than the screen. What matters are the coordinates of the point, and the scale of the overall application.
Now back to your cubes, don't try to insert 1px in between two adjacent ones. Your cubes are defined in terms of mm, so try to choose the distance in mm appropriate to your world, and let the rasterizer do its job and translate them to pixels.
I have been informed by two co-workers that I tracked down that this is indeed impossible using normal means.
I'd like to implement a Filter that allows resampling of an image by moving a number of control points that mark edges and tangent directions. The goal is to be able to freely transform an image as seen in Photoshop when you use "Free Transform" and chose Warpmode "Custom". The image is fitted into a some kind of Spline-Patch (if that is a valid name) that can be manipulated.
I understand how simple splines (paths) work but how do you connect them to form a patch?
And how can you sample such a patch to render the morphed image? For each pixel in the target I'd need to know what pixel in the source image corresponds. I don't even know where to start searching...
Any helpful info (keywords, links, papers, reference implementations) are greatly appreciated!
This document will get you a good insight into warping: http://www.gson.org/thesis/warping-thesis.pdf
However, this will include filtering out high frequencies, which will make the implementation a lot more complicated but will give a better result.
An easy way to accomplish what you want to do would be to loop through every pixel in your final image, plug the coordinates into your splines and retrieve the pixel in your original image. This pixel might have coordinates 0.4/1.2 so you could bilinearly interpolate between 0/1, 1/1, 0/2 and 1/2.
As for splines: there are many resources and solutions online for the 1D case. As for 2D it gets a bit trickier to find helpful resources.
A simple example for the 1D case: http://www-users.cselabs.umn.edu/classes/Spring-2009/csci2031/quad_spline.pdf
Here's a great guide for the 2D case: http://en.wikipedia.org/wiki/Bicubic_interpolation
Based upon this you could derive an own scheme for splines for the 2D case. Define a bivariate (with x and y) polynomial and set your constraints to solve for the coefficients of the polynomial.
Just keep in mind that the borders of the spline patches have to be consistent (both in value and derivative) to avoid ugly jumps.
Good luck!
I saw a question on reverse projecting 4 2D points to derive the corners of a rectangle in 3D space. I have a kind of more general version of the same problem:
Given either a focal length (which can be solved to produce arcseconds / pixel) or the intrinsic camera matrix (a 3x2 matrix that defines the properties of the pinhole camera model being used - it's directly related to focal length), compute the camera ray that goes through each pixel.
I'd like to take a series of frames, derive the candidate light rays from each frame, and use some sort of iterative solving approach to derive the camera pose from each frame (given a sufficiently large sample, of course)... All of that is really just massively-parallel implementations of a generalized Hough algorithm... it's getting the candidate rays in the first place that I'm having the problem with...
A friend of mine found the source code from a university for the camera matching in PhotoSynth. I'd Google around for it, if I were you.
That's a good suggestion... and I will definitely look into it (photosynth kind of resparked my interest in this subject - but I've been working on it for months for robochamps) - but it's a sparse implementation - it looks for "good" features (points in the image that should be easily identifiable in other views of the same image), and while I certainly plan to score each match based on how good the feature it's matching is, I want the full dense algorithm to derive every pixel... or should I say voxel lol?
After a little poking around, isn't it the extrinsic matrix that tells you where the camera actually is in 3-space?
I worked at a company that did a lot of this, but I always used the tools that the algorithm guys wrote. :)