I'm hoping to create a Voronoi landscape in Unity in C#. I looked at a number of Unity Project files, but they all implement Fortune's algorithm, which is completely over my head. Are there any other methods of generating Voronoi diagram (that is easier to understand)?
Slow performance is completely fine with me.
Much appreciated!
Sidenote: Since I'm working in Unity and need to generate 2D/3D mesh from Voronoi diagram, per-pixel distance check won't work :,(
On second thought, maybe I could use a 2D array of Vector2s instead of pixels, that are 1.0 unit spaced apart in x and z axis.
There is a very simple way to create an approximated Voronoi diagram VD. For every Site s that should define a cell in the VD (2D-plane) you center a cone at s with constant slope and a certain height. Then you look from above onto that landscape of cones (where all the spikes are visible). The boundary where the different cones meet (projected to the 2D-plane) is the (approximated) Voronoi diagram.
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As you requested in the comments, to get the actual edge data seems not so easy. But there could be some graphical routines to generate them by intersecting the cones.
An alternative is to compute a Delaunay triangulation of the given point set. There are some implementation referenced in this related post (also simple approximations are mentioned). Then you compute the dual graph of your triangulation and you have the Voronoi diagram. (Dual graph means that for every for every edge AB in the triangulation there exists an edge in the VD bisecting the space between the two vertices A and B, and for every triangle there exists a vertex in the VD where the dual edges meet.) Othwerwise there are also many C# Voronoi implementations around: Unity-delaunay, but as you mentioned using the Fortune approach.
If you want to code everything yourself you may compute a triangulation of the points with brute force for n points in O(n^2) time. Then apply in-circle tests and edge flips. That is, for every triangle t(abc) create a circle C defined by the three vertices of t. Then check if there lies another point d of your point set inside C. If so, then flip the edge that is in t as well as forms an edge in the triangle with d. This flipping is done until all triangles fulfil the empty circle property (Delaunay condition). Again with brute force will take O(n^2) time. Then you can compute the dual graph as mentioned above.
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"Easiest? That's the brute-force approach: For each pixel in your output, iterate through all points, compute distance, use the closest. Slow as can be, but very simple. If performance isn't important, it does the job."
[1] Easiest algorithm of Voronoi diagram to implement?
Related
I am currently developing an indoor path-finding. I have multiple floors and different rooms. How will I be able to implement a* algorithm in the images of each floor using c# wpf?
I use spatial A* for the game I'm working on.
Spatial A* uses movement "cost" to work out the best route between two points. The cost mentions is supplied by an array. Usually a 2d array of number - float uint or whatever.
Moving through a square/cell at position x,y thus costs the number in that 2d array. EG costs[2,3] would be the cost of movement through the cell 2 cells across from the left and 3 down from the top of an imaginary grid projected onto your "room".
If the move is diagonal then there's also a multiplier to consider but that will be in whichever implementation you go with.
Hence you need a 2d costed array per floor.
You would need to somehow analyse your pictures and work out an appropriate size for a costed cell. This should match the smallest size of a significant piece of terrain in your floor.
You would then translate your picture into a costed array. You've not told us anywhere near enough to tell you specifically how to do that. Maybe that would have to be a manual process though.
Blocked cells get the max number, empty cells get 1. Depending on your requirements that might be that. Or alternatively you might have actors leaping tables and chairs etc.
You give the pathing algorithm start and target location (x,y), the appropriate costed array and it works out the cheapest route.
I'm trying to construct a program in C# that generates a 3D model of a structure composed of beams, and then creates some views of the object (front, side, top and isometric).
As I don't need to draw surfaces (the edges are enough), I've been calculating each line to draw, and then do it with
GraphicObject.DrawLine(myPen, x1, y1, x2, y2)
This worked fine so far, but as I get adding parts to the structure, the refresh of GraphicObject takes too much time. So I'm getting into line visibility check to reduce the amount of lines to draw.
I've searched Wikipedia and some PDFs on the subject, but all I found is oriented by surfaces. So my question: Is there a simplified algorithm to check visibility of object edges, or should i go for a different approach, like considering surfaces?
Any suggestions would be appreciated, thanks for your help.
Additional notes/questions:
My current approach:
calculate every beam in a local axis (all vertices)
=> move them to their global position
=> create a list with pairs of points (projected and scaled to the view)
=> GraphicObject.DrawLine the list of point pairs)
would the whole thing be faster if I'd calculate the view by pixels rather than using the DrawLine method?
Screenshots follow with the type of structure it's going to do (not fully complete yet):
Structure view
Structure detail
There are 2 solutions to improve the performance.
a) switch the computation to the graphics card.
b) Use a kd-tree or some other similar data structure to quickly remove the non visible edges.
Here's more details:
For a), a lot of you computations are multiplying many vertices (vectors of length 3) by some matrix. The CPUs are slow because they only do a couple of these operations at a time. Switch to a GPU, for example using CUDA, which will allow you to do them more in parallel, with better memory access infrastructure. You can also use OpenGL/DirectX/Vulkan or whatever to render the lines themselves to skip having to get the results back from the graphics card and whatever other hiccups get introduced by windows code/libraries. This will help in almost all cases to improve performance.
For b), it only helps when you are not looking at the entire scene (in that case you really need to draw everything). In this cases you can store you scene in a kd-tree or some other data structure and use it to quickly remove things that are for sure outside of the view area. You usually need to intersect some cuboid with a pyramid/fustrum so there's more math involved.
As a compromise that should help in a large scenes where you want to see everything you can consider adjusting the level of detail. From your example, the read beans across are composed of 8 or so components. If you are far enough you are not going to be able to distinguish the 8, so just draw one. This will work great if you have a large number of rounded edges as you can simplify a lot of them.
I have 3D point clouds and want to reconstruct the surface. I tried various techniques in Meshlab to find the algorithm that best suits my specific kind of cloud.
The poisson surface reconstruction is very promising, but it does not preserve the original point position. After reconstruction and measuring at specific positions in the cloud it turned out that the measurements are off by a factor of over 1.5 compared to measurements on the object in the real world.
The ball pivoting algorithm is better. It preserved the position of the points and the measurements were also within the expected range. However this algorithm is patented in the USA so I can't use it for a commercial project.
After researching other algorithms, I did not find any that preserve the point position like ball pivoting which could be used in a commercial environment. Do you know algorithms that fulfill these two criteria and which I could try with my point cloud to see if they work well before implementing them?
Any help would be appreciated.
For interpolating surface reconstruction (that keeps the datapoints), two algorithms perform reasonably well (crust and co-cone).
Crust algorithm:
The idea is to first compute the Voronoi diagram of the pointset, then select from the Voronoi vertices the ones that are a good approximation of the medial axis (called the poles), then compute the 3D Delaunay triangulation of the input points + the poles, and finally extract the triangles that connect three input points in a tetrahedron where the fourth vertex is a pole.
More references:
http://web.cs.ucdavis.edu/~amenta/pubs/crust.pdf
http://web.mit.edu/manoli/crust/www/crust.html
plus: quite simple to implement, some theoretical guarantees if input data is a good sampling
minus: requires to compute two Delaunay triangulations
Co-cone algorithm:
The idea is to compute the Voronoi diagram of the pointset, and then in each Voronoi cell compute a good approximation of the normal to the surface (as the vector that connect the poles, i.e. the two Voronoi vertices furthest away from the datapoint). Then in each Voronoi cell one considers the complement of a cone (co-cone) centered on the datapoint and having the normal as an axis. If three co-cones have a non-empty intersection with a Voronoi edge, then the three datapoints are connected with a triangle. Note that the co-cone objects do not need to be constructed explicitely (just angles need to be compared in order to test whether there is an intersection).
More references:
http://web.cse.ohio-state.edu/~tamaldey/surfrecon.htm
Plus: requires a single Delaunay triangulation (compared to 2 for the Crust), some theoretical guarantees if the input data is a "good sampling"
Minus: a little bit more complicated than the crust (but worth the effort I think)
Some final words:
These algorithms construct a good (i.e. manifold) surface if the point set realises a good sampling (i.e. density proportional to thickness and curvature, something called "local feature size" = distance to medial axis). In practice, the input data does not satisfy this condition, therefore the output of the method will be a "soup of triangles" that will be mostly OK but that will require some post-processing to fix some local defects.
Edit 03/21/16 You may also try my own algorithm (Co3Ne), implemented in my software library Geogram (http://alice.loria.fr/software/geogram/doc/html/index.html) and my software Graphite (http://alice.loria.fr/software/graphite/doc/html/). Graphite can be downloaded there: http://gforge.inria.fr/frs/?group_id=1465 (both portable source code and Windows64 executable). It is a form of Co-cone with various optimizations and parallel implementation.
I am trying to write an algorithm (in c#) that will stitch two or more unrelated heightmaps together so there is no visible seam between the maps. Basically I want to mimic the functionality found on this page :
http://www.bundysoft.com/wiki/doku.php?id=tutorials:l3dt:stitching_heightmaps
(You can just look at the pictures to get the gist of what I'm talking about)
I also want to be able to take a single heightmap and alter it so it can be tiled, in order to create an endless world (All of this is for use in Unity3d). However, if I can stitch multiple heightmaps together, I should be able to easily modify the algorithm to act on a single heightmap, so I am not worried about this part.
Any kind of guidance would be appreciated, as I have searched and searched for a solution without success. Just a simple nudge in the right direction would be greatly appreciated! I understand that many image manipulation techniques can be applied to heightmaps, but have been unable to find a image processing algorithm that produces the results I'm looking for. For instance, image stitching appears to only work for images that have overlapping fields of view, which is not the case with unrelated heightmaps.
Would utilizing a FFT low pass filter in some way work, or would that only be useful in generating a single tileable heightmap?
Because the algorithm is to be used in Unit3d, any c# code will have to be confined to .Net 3.5, as I believe that's the latest version Unity uses.
Thanks for any help!
Okay, seems I was on the right track with my previous attempts at solving this problem. My initial attemp at stitching the heightmaps together involved the following steps for each point on the heightmap:
1) Find the average between a point on the heightmap and its opposite point. The opposite point is simply the first point reflected across either the x axis (if stitching horizontal edges) or the z axis (for the vertical edges).
2) Find the new height for the point using the following formula:
newHeight = oldHeight + (average - oldHeight)*((maxDistance-distance)/maxDistance);
Where distance is the distance from the point on the heightmap to the nearest horizontal or vertical edge (depending on which edge you want to stitch). Any point with a distance less than maxDistance (which is an adjustable value that effects how much of the terrain is altered) is adjusted based on this formula.
That was the old formula, and while it produced really nice results for most of the terrain, it was creating noticeable lines in the areas between the region of altered heightmap points and the region of unaltered heightmap points. I realized almost immediately that this was occurring because the slope of the altered regions was too steep in comparison to the unaltered regions, thus creating a noticeable contrast between the two. Unfortunately, I went about solving this issue the wrong way, looking for solutions on how to blur or smooth the contrasting regions together to remove the line.
After very little success with smoothing techniques, I decided to try and reduce the slope of the altered region, in the hope that it would better blend with the slope of the unaltered region. I am happy to report that this has improved my stitching algorithm greatly, removing 99% of the lines reported above.
The main culprit from the old formula was this part:
(maxDistance-distance)/maxDistance
which was producing a value between 0 and 1 linearly based on the distance of the point to the nearest edge. As the distance between the heightmap points and the edge increased, the heightmap points would utilize less and less of the average (as defined above), and shift more and more towards their original values. This linear interpolation was the cause of the too step slope, but luckily I found a built in method in the Mathf class of Unity's API that allows for quadratic (I believe cubic) interpolation. This is the SmoothStep Method.
Using this method (I believe a similar method can be found in the Xna framework found here), the change in how much of the average is used in determining a heightmap value becomes very severe in middle distances, but that severity lessens exponentially the closer the distance gets to maxDistance, creating a less severe slope that better blends with the slope of the unaltered region. The new forumla looks something like this:
//Using Mathf - Unity only?
float weight = Mathf.SmoothStep(1f, 0f, distance/maxDistance);
//Using XNA
float weight = MathHelper.SmoothStep(1f, 0f, distance/maxDistance);
//If you can't use either of the two methods above
float input = distance/maxDistance;
float weight = 1f + (-1f)*(3f*(float)Math.Pow(input, 2f) - 2f*(float)Math.Pow(input, 3f));
//Then calculate the new height using this weight
newHeight = oldHeight + (average - oldHeight)*weight;
There may be even better interpolation methods that produce better stitching. I will certainly update this question if I find such a method, so anyone else looking to do heightmap stitching can find the information they need. Kudos to rincewound for being on the right track with linear interpolation!
What is done in the images you posted looks a lot like simple linear interpolation to me.
So basically: You take two images (Left, Right) and define a stitching region. For linear interpolation you could take the leftmost pixel of the left image (in the stitching region) and the rightmost pixel of the right image (also in the stitching region). Then you fill the space in between with interpolated values.
Take this example - I'm using a single line here to show the idea:
Left = [11,11,11,10,10,10,10]
Right= [01,01,01,01,02,02,02]
Lets say our overlap is 4 pixels wide:
Left = [11,11,11,10,10,10,10]
Right= [01,01,01,01,02,02,02]
^ ^ ^ ^ overlap/stitiching region.
The leftmost value of the left image would be 10
The rightmost value of the right image would be 1.
Now we interpolate linearly between 10 and 1 in 2 steps, our new stitching region looks as follows
stitch = [10, 07, 04, 01]
We end up with the following stitched line:
line = [11,11,11,10,07,04,01,02,02,02]
If you apply this to two complete images you should get a result similar to what you posted before.
This seems non-trivial (it gets asked quite a lot on various forums), but I absolutely need this as a building block for a more complex algorithm.
Input: 2 polygons (A and B) in 2D, given as a list of edges [(x0, y0, x1, y2), ...] each. The points are represented by pairs of doubles. I do not know if they are given clockwise, counter-clockwise or in any direction at all. I do know that they are not necessarily convex.
Output: 3 polygons representing A, B and the intersecting polygon AB. Either of which may be an empty (?) polygon, e.g. null.
Hint for optimization: These polygons represent room and floor boundaries. So the room boundary will normally fully intersect with the floor boundary, unless it belongs to another floor on the same plane (argh!).
I'm kind of hoping someone has already done this in c# and will let me use their strategy/code, as what I have found so far on this problem is rather daunting.
EDIT: So it seems I'm not entirely chicken for feiling faint at the prospect of doing this. I would like to restate the desired output here, as this is a special case and might make computation simpler:
Output: First polygon minus all the intersecting bits, intersection polygons (plural is ok). I'm not really interested in the second polygon, just its intersection with the first.
EDIT2: I am currently using the GPC (General Polygon Clipper) library that makes this really easy!
Arash Partow's FastGEO library contains implementations of many interesting algorithms in computational geometry. Polygon intersection is one of them. It's written in Pascal, but it's only implementing math so it's pretty readable. Note that you will certainly need to preprocess your edges a little, to get them into clockwise or counterclockwise order.
ETA: But really, the best way to do this is to not do this. Find another way to approach your problem that doesn't involve arbitrary polygon intersections.
If you are programming in .NET Framework, you may want to take a look at SqlGeometry class available in .NET assemblies shipped as Microsoft SQL Server System CLR Types
The SqlGeometry class provides STIntersection method
SqlGeometry g1 = SqlGeometry.Parse("POLYGON ((...))");
SqlGeometry g2 = SqlGeometry.Parse("POLYGON ((...))");
SqlGeometry intersection = g1.STIntersection(g2);
What I think you should do
Do not attempt to do this yourself if you can possibly help it. Instead, use one of the many available polygon intersection algorithms that already exist.
I was strongly considering the following codebase on the strength of their demonstration code and the fact that they mentioned their handling of most/all of the weird cases. You would need to donate an amount (of you/your company's choice) if you use it commercially, but it's worth it to get a robust version of this kind of code.
http://www.cs.man.ac.uk/~toby/gpc/
What I actually did was to use a polygon-intersection algorithm that is part of the Java2D libraries. You can possibly find something similar in MS's own C# libraries to use.
There are other options out there as well; look for "polygon clipper" or "polygon clipping", since the same basic algorithms that handle polygon intersection also tend to be usable for the general clipping cases.
Once you actually have a polygon clipping library, you just need to subtract polygon B from polygon A to get your first piece of output, and intersect polygons A and B to get your second piece of output.
How to roll your own, for the hopelessly masochistic
When I was considering rolling my own, I found the Weiler-Atherton algorithm to have the most potential for general polygon-cutting. I used the following as a reference:
http://cs1.bradley.edu/public/jcm/weileratherton.html
http://en.wikipedia.org/wiki/Weiler-Atherton
The details, as they say, are too dense to include here, but I have no doubt that you'll be able to find references on Weiler-Atherton for years to come. Essentially, you split all the points into those that are entering the final polygon or exiting the final polygon, then you form a graph out of all the points, and then walk the graph in the appropriate directions in order to extract all the polygon pieces you want. By changing the way you define and treat the "entering" and "exiting" polygons, you can achieve several possible polygon intersections (AND, OR, XOR, etc.).
It's actually fairly implementable, but like with any computational geometry code, the devil is in the degeneracies.
You may also want to have a look at the NetTopologySuite or even try importing it into Sql server 2008 & it's spatial tools.
Clipper is an open source freeware polygon clipping library (written in Delphi and C++) that does exactly what you're asking - http://sourceforge.net/projects/polyclipping/
In my testing, Clipper is both significantly faster and far less prone to error than GPC (see more detailed comparisons here - http://www.angusj.com/delphi/clipper.php#features). Also, while there's source code for both Delphi and C++, the Clipper library also includes a compiled DLL to make it very easy to use the clipping functions in other (Windows) languages too.
A polygon is fully described by an ordered list of points (P1, P2, ..., Pn). The edges are (P1 - P2), (P2 - P3), ..., (Pn - P1). If you have two polygons A and B which overlaps, there will be a point An (from the list on points describing polygon A) which lies within the area surrounded by polygon B or vice versa (a point of B lies in A). If no such point is found, then the polygons does not overlap. If such a point is found (i.e. Ai) check the adjacent points of the polygon A(i-1) and A(i+1). Repeat until you find a point outside the area or all points are checked (then the first polygon lies completly within the second polygon). If you found a point outside then you can calculate the crossing point. Find the corresponding edge of polygon B and follow it with resersed roles = now check if the points of polygon B lie within polygon A. This way you can build a list of points which describe the overlapping polygon. If needed you should check if the polygons are identical, (P1, P2, P3) === (P2, P3, P1).
This is just an idea and there maybe better ways. If you find a working and tested solution I would recommend that you use it...
narozed
Try to use GIS tools for that, such as ArcObjects, TopologySuite, GEOS, OGR, etc. I'm not sure if all of these distributions are availuable to .net, but they all do the same.
This academic paper explains how to do this.
If you dare to take a look to other people's GPL C++ code, you can see how do they do it in Inkscape:
http://bazaar.launchpad.net/~inkscape.dev/inkscape/trunk/view/head:/src/2geom/shape.cpp (line 126)