I have a 2D-delaunay-triangulation where each vertex is labeled with an elevation. I now want to remove vertices from the triangulation without making big changes to the form (analogous to douglas-peucker for polylines).
There are a lot of mesh-coarsening algorithms for 3D-meshes. But isn't there something simpler for my task?
Do not remove points from your existing model. Instead construct a second one. Start with a few convex hull points and then refine the new model in a divide and conquer style until comparison with the original model yields that the specified error bound is kept. I have implemented it like that in the Fade library and it works well. You can try my 2.5D Douglas-Peucker implementation if you want, the student license is free.
But best possible output quality requires also that feature lines are detected, simplified and conserved. This is more involved, I work on that topic and hope that I can provide results soon.
Related
Say I had a point cloud with n number of points in 3d space(relatively densely packed together). What is the most efficient way to create a surface that goes contains every single point in it and lets me calculate values such as the normal and curvature at some point on the surface that was created? I also need to be able to create this surface as fast as possible(a few milliseconds hopefully working with python) and it can be assumed that n < 1000.
There is no "most efficient and effective" way (this is true of any problem in any domain).
In the first place, the surface you have in mind is not mathematically defined uniquely.
A possible approach is by means of the so-called Alpha-shapes, implemented either from a Delaunay tetrahedrization, or by the ball-pivoting method. For other methods, lookup "mesh reconstruction" or "surface reconstruction".
On another hand, normals and curvature can be computed locally, from neighbors configurations, without reconstructing a surface (though there is an ambiguity on the orientation of the normals).
I could suggest Nina Amenta's Power Crust algorithm (link to code), or also meshlab suite, which can compute the curvatures too.
I am currently trying to construct the area covered by a device over an operating period.
The first step in this process appears to be constructing a polygon of the covered area.
Since the pattern is not a standard shape, convex hulls overstate the covered area by jumping to the largest coverage area possible.
I have found a paper that appears to cover the concept of non-convex hull generation, but no discussions on how to implement this within a high level language.
http://www.geosensor.net/papers/duckham08.PR.pdf
Has anyone seen a straight forward algorithm for constructing a non-convex hull or concave hull or perhaps any python code to achieve the same result?
I have tried convex hulls mainly qhull, with a limited edge size with limited success.
Also I have noticed some licensed libraries that will not be able to be distributed, so unfortunately thats off the table.
Any better ideas or cookbooks?
You might try looking into Alpha Shapes. The CGAL library can compute them.
Edit: I see that the paper you linked references alpha shapes, and also has an algorithm listing. Is that not high level enough for you? Since you listed python as a tag, I'm sure there are Delaunay triangulation libraries in Python, which I think is the hardest part of implementing the algorithm; you just need to make sure you can modify the resulting triangulation output. The boundary query functions can probably be implemented with associative arrays.
I am looking for an algorithm that given two meshes could clip one using another.
The simplest form of this is clipping a mesh using a plane. I've already implemented that by following something similar to what is described here.
What it does is basically inspecting all mesh vertices and triangles with respect to the plane (the plane's normal and point are given). If the triangle is completely above the plane, it is left untouched. If it falls completely below the plane, it is discarded. If some of the edges of the triangle intersect with the plane, the intersecting points with the plane are calculated and added as the new vertices. Finally a cap is generated for the hole on the place the mesh was cut.
The problem is that the algorithm assumes that the plane is unlimited, therefore whatever is in its path is clipped. In the simplest form, I need an extension of this without the assumption of a plane of "infinite" size.
To clarify, imagine that we have a 3D model of a desk with 2 boxes on it. The boxes are adjacent (but not touching or stacked). The user will define a cutting plane of a limited width and height underneath the first box and performs the cut. We end up with a desk model (mesh) with a box on it and another box (mesh) that can be freely moved around/manipulated.
In the general form, I'd like the user to be able to define a bounding box for the box he/she wants to separate from the desk model and perform the cut using that bounding box.
If I could extend the algorithm I already have to an algorithm with limited-sized planes, that would be great for now.
What you're looking for are constructive solid geometry/boolean algorithms with arbitrary meshes. It's considerably more complex than slicing meshes by an infinite plane.
Among the earliest and simplest research in this area, and a good starting point, is Constructive Solid Geometry for Polyhedral Objects by Trumbore and Hughes.
http://cs.brown.edu/~jfh/papers/Laidlaw-CSG-1986/main.htm
From the original paper:
More elaborate solutions extend upon this subject with a variety of data structures.
The real complexity of the operation lies in the slicing algorithm to slice one triangle against another. The nightmare of implementing robust CSG lies in numerical precision. It's easy when you involve objects far more complex than a cube to run into cases where a slice is made just barely next to a vertex (at which point you have the tough decision of merging the new split vertex or not prior to carrying out more splits), where polygons are coplanar (or almost), etc.
So I suggest initially erring on the side of using very high-precision floating point numbers, possibly even higher than double precision to focus on getting something working correctly and robustly. You can optimize later (first pass should be to use an accelerator like an octree/kd-tree/bvh), but you'll avoid many headaches this way in your first iteration.
This is vastly simpler to implement at render time if you're focusing on a raytracer rather than a modeling software, e.g. With raytracers, all you have to do to do this kind of arbitrary clipping is pretend that an object used to subtract from another has its polygons flipped in the culling process, e.g. It's easy to solve robustly at the ray level, but quite a bit harder to do robustly at the geometric level.
Another thing you can do to make your life so much easier if you can afford it is to voxelize your object, find subtractions/additions/unions of voxels, and then translate the voxels back into a mesh. This is so much easier to make robust, but harder to do efficiently and the voxel->polygon conversion can get quite involved if you want better results than what marching cubes provide.
It's a really tough area to do extremely well and requires perseverance, and thus the reason for the existence of things like this: http://carve-csg.com/about.
If someone is interested, currently there is a solution for this problem in CGAL library. It allows clipping one triangular mesh using another mesh as bounding volume. The usage example can be found here.
From what I understand, taking a polygon and breaking it up into composite triangles is called "tesselation". What's the opposite process called and can anyone link me to an algorithm for it?
Essentially, I have a list of 2D triangles and I need an algorithm to recombine them into a polygon.
Thanks!
I think you need to transform your triangles as a half edge data structure, and then you should be able to easily find the half edges which have no opposite.
It's called mesh decimation. Here is some code I wrote to do this for a class. Tibur is correct that the half edge data structure makes this much more efficient.
http://www.cs.virginia.edu/~mjh7v/advgfx/proj1/
The thing that you are calling tessellation is actually called triangulation. The thing you are searching for is tessellation (you may have heard of it referred to as tiling).
If you are more specific about the problem you are trying to solve (e.g. do you know the shape of the final polygon?) I can try to recommend some more specific algorithms.
I'd like to write a program that lets me arbitrarily distort a textured polygon by dragging its vertices. I want the texture to distort fluidly and without overlap, assuming the new polygon doesn't intersect itself. I should also be able to repeat the process with the new shape, and with a minimum amount of loss.
Are there any algorithms for doing this?
It sounds like you might want a variation on the Schwarz-Christoffel mapping. This is a type of conformal mapping that can be used to warp a polygon to and from a simpler region, like a disk; although I have not implemented it, apparently it is computationally tractable.
For your application, you would set up a map from the original polygon to the simpler region, and compute the inverse map to the modified polygon; combining the two should give you a nice conformal mapping from the original to the modified polygon.
Conformal mappings are nice and smooth, but they can sometimes behave in unintuitive ways; I can imagine that an animated version might yield some entertaining "slidy" effects. The conformal mapping will preserve local angles in the interior of the polygon; this means that the size distortion very near a modified vertex can be severe.
People have been working on solutions to this problem for the past decade or two, and the state of the art keeps on getting better and better (but the math gets harder as well). A good place to start (and sort of where I stopped following it) is the work http://www.cs.technion.ac.il/~weber/Publications/Complex-Coordinates/
Read the paper there, and look up the papers in the references. One of them should give you an algorithm that you're willing to implement.
The simplest method I can think of is to triangulate the input polygon (using an ear clipping method, or something similarly good) and then move the points. Then you can use a barycentric mapping from the original polygon to the new space.
If you're looking for something more robust, you might look at mean value coordinates.