I am looking for an algorithm to find all (or maximum no of) contiguous faces of a continuous mesh. The faces should be ordered in an array in such a way that each face is preceded by a face linked to it on the mesh. The ultimate goal is to have a single such array. Is it possible even in theory? If not what's the best way to maximize the count of faces in the array?
In this (rather naive) implementation the selection point traverses clockwise covering end vertex of the available edge of the last face covered. But this quickly gets into a dead-end. I also tried both the ends of the edge, or all the available vertices of the face, but sooner or later each one reaches a face with no connections to un-selected faces.
Edit:
It's a triangulated mesh, i.e. each face has exactly three vertices. And the requirement is to have a set of minimum no of arrays (ideally one) covering all the connected faces of the mesh.
This is a hard problem (the Hamiltonian path problem in a planar graph (specifically, the dual of the input graph)), but you may have get good results with a local search method. There's a simple one due to Angluin and Valiant (https://doi.org/10.1016/0022-0000(79)90045-X) and a more complicated effort by Frieze (https://doi.org/10.1002/rsa.20542). These algorithms are proved in theory to work on random graphs only, but graphs without adversarial construction are often amenable too.
Related
I've been researching for a known algorithm that identifies the "most relevant" vertices of a 2D polygon. I may be using the wrong keywords (I've been trying to search for mesh simplification algorithms), but I've not yet found anything useful.
I should define what I mean by "most relevant" vertices with some context. I want to take a 2D polygon, apply a geometrical transformation, and render both the pre-transformed and post-transformed polygons with a mapping between the vertices to visualize the effects of the transformation. However, with small highly detailed polygons (high vertex count per area), there is a lot of "visual clutter".
The idea is that there should be an algorithm that could identify which vertices would be eligible for mapping and which ones wouldn't. I can design such an algorithm by taking into account two things:
Edge length: ignore a vertex if the length between it and the previous one is smaller than a threshold. An accumulator would be needed to avoid ignoring multiple subsequent vertices.
Internal angle: ignore a vertex if the internal angle at the vertex is higher than a threshold. An "accumulator" would be needed to avoid ignoring multiple subsequent vertices.
Despite probably being able to implement such a thing, I don't like reinventing the wheel and decided to ask you if you came across something like this which could actually solve other problems that I didn't think of (e.g., complex polygons).
It sounds like you're looking for the Ramer-Douglas-Peucker algorithm, which does "path simplification" but can be extended for use with polygons. It works by starting with only a couple of endpoints, then greedily adding back whichever vertices are necessary to approximate the original shape to within a certain tolerance. There are a variety of other algorithms and heuristics, but none of them has a reputation for reliably producing significantly better results than RDP, and RDP is easy to understand and implement.
I can't find a universal solution to "The Spider and the Fly Problem" (the shortest path between two points on a cuboid on its surface). Everybody solves a one specific case but what when two points can be anywhere?
My idea was to create an algorithm that considers various nets of a cuboid, calculates shortest paths on 2D and then returns the minimum but I have no idea for the algorithm to generate these grids (I guess hardcoding all combinations is not the best way).
Simplistic approach (only works where the points are on the same or adjacent faces)
Flatten the cube structure to 2d as follows...
Start with a face containing one of the two points. If this also contains the other point, you can stop there and the solution is trivial.
There are only 4 neighbouring faces. If any of them contain the other point, you can place that face adjoining the first, and plot the straight line.
Otherwise, then the points are on opposing faces. You need to try placing the final face adjoining each of the 4 neighbouring faces, and choose the shortest of the 4 alternatives. This will not always give the best solution, but it's not far off, and is cheap.
Generic approach
Jim Propp's surface distance conjecture is that For a centrally symmetric convex compact body, the greatest surface distance between two points is achieved only for pairs which are opposites through the centre. My conjecture based on that would be that the shortest distance is approximately where the plane made by the two points and the centre of the body meets the surface. So you simply need to find where that plane intersects the faces using 3d geometry, and use the faces that are crossed by the shorter of the two alternatives when looking at possible routes. If the plane runs along an edge of the cube (e.g. if the points are on opposite faces and are both between the centre of the face and the corner of the face, and those corners are linked by an edge) then routes through both faces should be considered, although I speculate they will be equivalent lengths.
This solution is more generic, and also satisfies scenarios where the points are on the same face, connected faces and opposite faces.
The only problem with this approach arises where the line between the two points passes through the centre of the body, which by definition means that the two points are exactly opposite each other, because that means the 3 points are in a straight line, so there isn't a plane...
I think this is a good question, for which the answer is not at all obvious. In the smooth realm, it is an extraordinarily difficult problem. Geodesics (shortest paths) on a sphere (which is a smooth analog of a cube) are easy to find. Geodesics on a biaxial ellipsoid (an ellipsoid of revolution; one cross section is a circle) are much harder to find. Finding geodesics on a triaxial ellipsoid (a smooth analog of a general cuboid) was a challenging unsolved problem in the first half of the 19th century. See the Wikipedia page.
On the other hand, geodesics on cuboid are made from straight line segments so are much simpler. But some of the difficulty of the problem remains.
You may be able to find some literature on the subject if you search for the term "net". A polyhedron cut along some edges so that it can be flattened is often called a "net". I was able to quickly find a site that claims (without proof) that there are just 11 different nets for a cub(oid). But I agree with you that hard coding all the variations is not the best way.
It's not even obvious to me that the approach using nets will work for all polyhedra. I think I see an argument that will work for cuboids, but for general polyhedra, even convex polyhedra, it is not known whether they must have even one net. See the Wikipedia page. I think a satisfying solution to the problem on cuboids should work more generally on polyhedra, and the net idea seems to be insufficiently general, in my view.
What I'm thinking might work is a dynamic programming solution, where you look at the different edges your path can pass through between the initial and final points. There is a hierarchy of edges (those on the starting face; those containing a vertex on the starting face; those on the faces adjacent to the starting face; etc.). For each point on each of those edges you can find the minimum distance to the start point, culminating in the minimum distance from the end point to the start point.
Another way to think about this is to use something akin to the reflection principle, except instead of reflections, we use rotations in space which rotate the polyhedron about one of its edges so that the other face adjacent to the edge becomes coplanar with the starting face. Then we don't have to worry about whether we have a good net or not. You just pick a sequence of edges so that the final point is eventually rotated onto the plane of the initial face. The sequence of edges is finite because any loop is not part of a minimal path. I'll think about how I might be able to communicate this idea better.
I solved the problem for cubes and cuboids by discretizing the cube edges, generating a big graph and solving graph shortest path problem. You can specify start point (sx, sy, 0), and algorithm will determine all shortest path to target points on top face (z = 1), here for 19 * 19 target points. Cube edges are divided into 100 parts. Graph with these settings has n=1558 vertices and m=464000(!) edges, inner loop of floyd_warshall_path() for updating shortest path distances is executed n³ = 3,781,833,112 times (takes less than 1 minute on Raspberry Pi400). Orange shortest paths flow through 3 cuboid faces, blue ones through 4. Algorithm generates OpenSCAD file as output. Details in this posting, all code in GitHub repo.
P.S:
I made experiments with 1 x 1 x 3 cuboid and was able to find examples where shortest path between two points needs to pass 5 faces. Code is submitted to GitHub repo, and details are in this forum posting.
Orange shortest paths are passing 3 faces, blue are passing 4 faces, and the new yellow shortest paths pass 5 faces! With "mirror" at bottom, allowing to see the bottom face with start point as well. This time cuboid edges are divided into 150 parts (149 inner vertices), and there are 49 * 49 top face target points for single start point on bottom face:
I implemented cuboid shortest paths completely different this time, no graph, and geometric distance calculations in 28 possible foldings of cuboid into plane, details in this new forum thread:
Efficient cuboid surface shortest path problem application
The much increased efficiency with all the sliders allows to change x/y coordinate of bottom face point, number of divisions in X/Y direction and which folding to display, with instantaneous display after any change. This allows to play with a cuboid and "see" how the shortest paths change (on top face as well as on side and bottom faces).
Scale to 50% size, 0.5fps animation shows the 6 foldings containing any shortest path. The animation corresponds to clockwise traversal of OpenSCAD 3D top face shown on the right.
With added top and bottom face view.
but for anyone still interested, this question was solved on stackxechange by "Intelligenti pauca" (with some nice diagrams): here is the original link
https://math.stackexchange.com/questions/3023721/finding-the-shortest-path-between-two-points-on-the-surface-of-a-cube
The "Simplistic approach" from "Richardissimo" was on the right track, you just need to check a few more cases.
Intelligenti pauca:
If the two points belong to adjacent faces, you have to check three
different possible unfoldings to find the shortest path. In diagram
below I represented the first point (red) and the second point (black)
in three possible relative positions: middle position occurs when the
path goes through the common edge, in the other cases the path
traverses one of the faces adjacent to both faces. The other possible
positions are clearly longer than these.
image1
If the two points belong to opposite faces, then 12 different possible
positions have to be checked: see diagram below.
image2
After mapping the points like this you can calculate the distances like normal on a plane an have min(possible distances) as your shortest path-length.
I am trying to learn using CGAL. I have questions about which data structures and triangulation schemes to use for my problem.
Problem description:
I have a small number ( < 1000) of particles moving on a sphere. I need to make a triangular Delaunay mesh out of this point cloud. At every time step, I need to:
Remesh the point cloud, only if required, so that the Delaunay criterion still holds. Store the mesh connectivity independently of the point coordinates.
Keeping the topology fixed, do some optimization using an iterative solver to calculate new particle positions. The number of solver iterations can be 100 or more with the same connectivity. At each iteration, the calculations require area of each triangle and some calculations on vertices connected by an edge (i.e. each vertex interacts with first ring of nearest neighbors).
Questions:
How can I change the coordinates of the points associated with mesh (triangulation data structure, surface mesh, polyhedra etc.) vertices without invalidating the iterators or circulators of the triangulation?
How to check when remeshing is required?
Which data structure gives fastest access to all edges and faces in a single pass over the full mesh? Every edge is shared between two triangles. The calculations on the edges are the most expensive. Hence, I want to calculate for each edge only once. Iterating once over all faces and separately over all edges may be less efficient.
Please let me know if any more information is required.
Giving part answers to your questions :
3/ You can use openmesh library to mesh your points. It allows one to reach the first ring of neighbours very fast as explained here, and also all edges and faces. I can't be sure if it is the data structure that gives the fastest access to these informations. To give you a hint of what speed to expect, In my work I use openmesh : running 30 'for' loops, each loop going over the first ring neighbours of the 500 000 vertices of my mesh and computing some arithmetics (typically center of gravity), takes in total less than 100ms.
1/ With openmesh, at any time you can reset a point position without changing its connectivity (it won't delete already defined edges and faces).
2/ To check if remeshing is needed, you have to check wether Delaunay condition is still satisfied at every point of your mesh. If it is not, remesh the whole or swap suitable edges.
Hope this helps!
I have a convex triangulated mesh. I am able to numerically calculate geodesics between points on the surface; however, I am having trouble tackling the following problem:
Imagine a net being placed over the mesh. The outside boundary of the net coincides with the boundary of the mesh, but the nodes of the net corresponding to the interior of the net are allowed to move freely. I'm interested in finding the configuration that would have the least stress (I know the distances for the at rest state of the net).
Doing this on a smooth surface is simple enough as I could solve for the stresses in terms of the positions of the nodes of the net; however, I don't see a way of calculating the stresses in terms of the position of the net nodes because I don't know that a formula exists for geodesics on a convex triangulated surface.
I'm hoping there is an alternative method to solving this such as a fixed point argument.
Hint:
If I am right, as long as a node remains inside a face, the equations are linear (just as if the node was on a plane). Assuming some node/face correspondence, you can solve for the equilibrium, as if the nodes did belong to the respective planes of support, unconstrained by the face boundaries.
Then for the nodes which are found to lie outside the face, you can project them on the surface and obtain a better face assignment. Hopefully this process might converge to a stable solution.
The picture shows a solution after a first tentative node/face assignment, then a second one after projection/reassignment.
On second thoughts, the problem is even harder as the computation involves geodesic distances between the nodes, which depend on the faces that are traversed. So the domain in which linearity holds when moving a single node is even smaller than a face, it is also limited by "wedges" emanating from the lined nodes and containing no other vertex.
Then you may have to compute the domains where the geodesic distances to a linked neighbor is a linear function of the coordinates and project onto this partition of the surface. Looks like an endeavor.
I have a complex polygon (possibly concave) and a few of its edges marked as entry/exit points. there is a possibility that inside this polygon may lie one or more blockades of arbitrary shape. what approaches could I use to determine whether a path of certain width exists between a pair of entry/exit edges?
having read through the question it looks like a homework type - it is not. I just wish to have a at least a few leads I could pursue, as this is new to me.
Take a look at Motion Planning - there's a wealth of information there.
It depends on if the route needs to have a width to it. If the object that has to move through has a finite size, you need to take the Minkowski difference of your domain polygon with the moving object's polygon, then you try to route through that.
One way to compute paths exactly is to compute the visibility graph of the polygon. The visibility graph has vertices corresponding to the vertices of the domain polygon (possibly with holes where the obstacles are), and two vertices are connected by an edge if they can "see" each other. The shape is passable if there exists a set of edges joining an entry to an exit. You can also compute things like shortest paths. Computing the visibility graph in a naive way is not hard, but slow. There are very advanced algorithms for doing it, but they (AFAIK) have not been implemented. I tried implementing a few several years ago, with only mediocre results. Most of them assume vertices in general position, using exact arithmetic, whereas practical applications would use floating point numbers.