For a game I am making I want to create 2D vines and vine like structures procedurally. Is there some paper or code snippet that someone can point me to?
Googling results in procedural trees which have straight spiky branches, but I need to create vines which are curvy. Think Jack and the beanstalk type of growth.
http://youtu.be/2wq541W6LyE?t=2m11s
Your particular approach is going to depend on how you game handles drawing and collisions.
An approach popular with flash-based games is to draw the vine to a bitmap: Since you don't list your programming environemnt, I'll just explain the steps, not the code.
Start with a circle,
1 draw it,
2 move it,
3 scale it down.
4 At a random interval, spawn a "branch" and or a leaf.
Set the scale and position of the branch to match the parent. Start a 1-5 loop on the branch.
5 repeat 1 until fully grown (scale is too small to proceed).
In the move phase it can be handy to use Sin curve to make your vine weave in and out.
Youcan tweak the settings for how much it curves to get different types of vines.
Here is a link to a discussion fo teh topic. Some good source to be found in the links.
http://groups.google.com/group/flashcodersny/browse_thread/thread/9906041e557e620c
Including source code inf flash:
http://xfiles.funnygarbage.com/~colinholgate/swf/varicoseg.zip
And a javascript version that looks more like lightning, but couild be adapted to vines without much change:
http://www.brainjam.ca/hyperbolic/01_spite_mrdoob.html
Related
I have successfully calculated Rotation, Translation with the intrinsic camera matrix of two cameras.
I also got rectified images from the left and right cameras. Now, I wonder how I calculate the 3D coordinate of a point, just one point in an image. Here, please see the green points. I have a look at the equation, but it requires baseline which I don't know how to calculate. Could you show me the process of calculating the 3d coordinate of the green point with the given information (R, T, and intrinsic matrix)?
FYI
1. I also have a Fundamental matrix and Essential matrix, just in case we need them.
2. Original image size is 960 x 720. Rectified ones are 925 x 669
3. The green point from the left image: (562, 185), from the right image: (542, 185)
The term "baseline" usually just means translation. Since you already have your rotation, translation and intrinsics matrices (let's not them R, T and K). you can triangulate and don't need either the Fundamental or Essential matrices (they could be used to extract R, T etc but you already have them). You don't really need your images to be rectified either, since it doesn't change the triangulation process that much. There are many ways to triangulate, each with their pros and cons, and many libraries that implement them. So, all I can do here is give you and overview of the problem and potential solutions, as well as pointers to resources that you can either use as their are or as a source of inspiration to write your own code.
Formalization and solution outlines. Let's formalize what we are after here. You have a 3d point X, with two observations x_1 and x_2 respectively in the left and right images. If you backproject them, you obtain two rays:
ray_1=K^{1}x_1
rat_2=R*K^{-1}x_2+T //I'm assuming that [R|T] is the pose of the second camera expressed in the referential of the first camera
Ideally, you'd want those two rays to meet at point X. Since in practice we always have some noise (discretization noise, rounding errors and so on) the two rays wont meet at X, so the best answer would be a point Q such that
Q=argmin_X {d(X,ray_1)^2+d(X,ray_2)^2}
where d(.) denotes the Euclidian distance between a line and a point. You can solve this problem as a regular least squares problem, or you can just take the geometric approach (called midpoint) of considering the line segment l that is perpendicular to both ray_1 and ray_2, and take its middle as your solution. Another quick and dirty way is to use the DLT. Basically, you re-write the constrains (i.e. X should be as close as possible to both rays) as a linear system AX=0 and solve it with SVD.
Usually, the geometric (midpoint) method is the less precise. The DLT based one, while not the most stable numerically, usually produces acceptable results.
Ressources that present in depth formalization
Hartley-Zisserman's book of course! Chapter 12. A simple DLT-based method, which is the one used in opencv (both in the calibration and sfm modules) is explained on page 312. It is very easy to implement, it shouldn't take more that 10 minutes in any language.
Szeliski'st book. It has an intersting discussion on triangulation in the chapter on SFM, but is not as straight-forward or in depth as Hartley-Zisserman's.
Code. You can use the triangulation methods from opencv, either from the calib3d module, or from the contribs/sfm module. Both use the DLT, but the code from the SFM module is more easily understandable (the calib3d code has a lot of old-school C code which is not very pleasant to read). There is also another lib, called openGV, which has a few interesting methods for triangulation.
cv::triangulatePoints
cv::sfm::triangulatePoints
OpenGV
The openGV git repo doesn't seem very active, and I'm not a big fan of the design of the library, but if I remember correctly (feel free to tell me otherwise) it offers methods other that the DLT for triangulations.
Naturally, those are all written in C++, but if you use other languages, finding wrappers or similar libraries wont be difficult (with python you still have opencv wrappers, and MATLAB has a bundle module, etc.).
I have an Ogre3D application and I would like to render a surface that represents the water with waves.
I think I am not the only one that has this purpose, so I was looking for an example to follow.
I imagine that if I want to create a water surface and want to move it like a wave I have to create a surface with many vertexes (according to which precision I want) and then control the height of each vertexes.
As the water will be quite big, I think that the water will take long time to be rendered, so I was wandering if it was better to render it by vertex or nurbs? Or are there any better way?
There's an Ocean example included in Ogre distribution that you can use as starting point. I don't remember if it uses any LOD system but it has quite nice random waves and Fresnel shader.
The nurbs won't help you much as there's no easy way to push them into GPU. They're good for some modelling tasks but at the end you need to convert them to 'real' geometry.
What I'm trying to achieve:
Have a look at the following image from this paper
It's taking a road graph that is likely represented as segments/junctions, giving the lines width (call it what you like, sweeping, thickening) and then generating triangulated geometry for the roads.
Why I am asking this question:
This operation seems to be a fairly standard thing to do, but I can't any papers that directly deal with how to do it. Most GIS / procedural city generation papers focus on the generation of the road graph itself (e.g. creating interesting topologies), but the step involving taking the graph data and generating triangle meshes / UVs is always glossed over.
Here's a really nice video of complex road intersections with nice texturing and good-looking junctions. This is the level of quality I'd eventually like to achieve, but an incremental step towards this would be more than acceptable to me. Here's another video showing interactive road graph creation with a 3d visualisation.
There is a paper to go with that video but nothing is said about the triangulation strategy :(
I have my own approach to try that's too long-winded to detail here, but I'd much rather implement an existing solution / algorithm if one exists, as it'll be better than anything I cook up in the next few weeks.
Can anyone point me in the right direction?
Thanks.
What you are seeking is the offset polygon for each of the regions bounded by roads. If all those regions are convex, this is an easy computation. If some are nonconvex, then it is more difficult, but still well-studied. You can find links at Wikipedia under straight skeleton, or here on StackOverflow under "An algorithm for inflating/deflating (offsetting, buffering) polygons."
What are some path finding algorithms used in games of all types? (Of all types where characters move, anyway) Is Dijkstra's ever used? I'm not really looking to code anything; just doing some research, though if you paste pseudocode or something, that would be fine (I can understand Java and C++).
I know A* is like THE algorithm to use in 2D games. That's great and all, but what about 2D games that are not grid-based? Things like Age of Empires, or Link's Awakening. There aren't distinct square spaces to navigate to, so what do they do?
What do 3D games do? I've read this thingy http://www.ai-blog.net/archives/000152.html, which I hear is a great authority on the subject, but it doesn't really explain HOW, once the meshes are set, the path finding is done. IF A* is what they use, then how is something like that done in a 3D environment? And how exactly do the splines work for rounding corners?
Dijkstra's algorithm calculates the shortest path to all nodes in a graph that are reachable from the starting position. For your average modern game, that would be both unnecessary and incredibly expensive.
You make a distinction between 2D and 3D, but it's worth noting that for any graph-based algorithm, the number of dimensions of your search space doesn't make a difference. The web page you linked to discusses waypoint graphs and navigation meshes; both are graph-based and could in principle work in any number of dimensions. Although there are no "distinct square spaces to move to", there are discrete "slots" in the space that the AI can move to and which have been carefully layed out by the game designers.
Concluding, A* is actually THE algorithm to use in 3D games just as much as in 2D games. Let's see how A* works:
At the start, you know the coordinates of your current position and
your target position. You make an optimistic estimate of the
distance to your destination, for example the length of the straight
line between the start position and the target.
Consider the adjacent nodes in the graph. If one of them is your
target (or contains it, in case of a navigation mesh), you're done.
For each adjacent node (in the case of a navigation mesh, this could
be the geometric center of the polygon or some other kind of
midpoint), estimate the associated cost of traveling along there as the
sum of two measures: the length of the path you'd have traveled so
far, and another optimistic estimate of the distance that would still
have to be covered.
Sort your options from the previous step by their estimated cost
together with all options that you've considered before, and pick
the option with the lowest estimated cost. Repeat from step 2.
There are some details I haven't discussed here, but this should be enough to see how A* is basically independent of the number of dimensions of your space. You should also be able to see why this works for continous spaces.
There are some closely related algorithms that deal with certain problems in the standard A* search. For example recursive best-first search (RBFS) and simplified memory-bounded A* (SMA*) require less memory, while learning real-time A* (LRTA*) allows the agent to move before a full path has been computed. I don't know whether these algorithms are actually used in current games.
As for the rounding of corners, this can be done either with distance lines (where corners are replaced by circular arcs), or with any kind of spline function for full-path smoothing.
In addition, algorithms are possible that rely on a gradient over the search space (where each point in space is associated with a value), rather than a graph. These are probably not applied in most games because they take more time and memory, but might be interesting to know about anyway. Examples include various hill-climbing algorithms (which are real-time by default) and potential field methods.
Methods to procedurally obtain a graph from a continuous space exist as well, for example cell decomposition, Voronoi skeletonization and probabilistic roadmap skeletonization. The former would produce something compatible with a navigation mesh (though it might be hard to make it equally efficient as a hand-crafted navigation mesh) while the latter two produce results that will be more like waypoint graphs. All of these, as well as potential field methods and A* search, are relevant to robotics.
Sources:
Artificial Intelligence: A Modern Approach, 2nd edition
Introduction to The Design and Analysis of Algorithms, 2nd edition
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I'm interested in creating poster-sized images that contain repeating patterns, similar to the two (public domain) images below, the Flower of Life and a Penrose tiling:
My questions:
How do people usually create images like these on a computer? I'm hoping the answer isn't, "Open Adobe Illustrator and guess at intersection points," since such points can be defined mathematically. But I also imagine that not everyone with an interest in geometric patterns is also familiar with programming.
What is the best environment for creating such images? In particular, what's the best way to get high-resolution images out of Java, Python, Processing, etc? Or, is Mathematica the best tool?
Actually calculating the points and doing the math isn't the hard part, in my mind (at least, it's not the focus of this question). I'm interested in the best way to get a high-quality visual product out of a program.
The best way to create images like these is to learn to write PostScript. It's a clean language, easy to learn, and quite powerful once you know it well.
Bill Casselman's manuals are by far the best reference for high quality mathematical illustration.
Use a vector image format like SVG. This will scale perfectly to any resolution.
Inkscape is a great tool for creating these.
Once you have a vector image format, there are many options for using it in programming languages, depending on your language of choice.
For example -
.NET - SvgNet
ActionScript - svgweb
C++ - LeadTools
XAML for WPF/Silverlight - ViewerSVG (Converts SVG to XAML)
I don't know how those images were created, I would guess they were scanned from a book, but, in my work with fractals, I tend to start with just using the <canvas> tag, mainly so that I can change the size of the element and see it drawn more iterations, so I can get the highest resolution.
That is the problem with something like SVG is that you would need to pick a resolution and then create it, and it will scale well up and down, but if you developed it at one resolution, then you go to a higher resolution to demo, you may see more gaps than you would like.
If you want to just do it and save it as a static image then any GUI will work, as you are saving a GIF at that point, but if you want it, for example, on a web page, and have it look as good as it can on that browser then you may want to look at using javascript.
The math part isn't hard, and so drawing the image is fairly easy, once you derive the recursive algorithm that is needed. I tend to go to the next iteration until the size is below a threshold, for example, a radius of < 3, then it exits.
Well, #2 is going to be kind of a holy war so I'll address #1. :)
The key to images of this nature is recursion. Basically they are the same image repeated over and over in a controlled way to get an intersting result. Take the flower of life for example. You repeat the center petal six times (the method to do the petals is up to you). Then you create six more flowers using the petals tip as the center and overlapping one of the petals. You then recursively move outward. After a few "rounds" you stop and draw the containing circle. Basically the recusion simulates the stamp, move and rotate that would be required if you were doing it by hand.
When I have played around with these kinds of things I have always found that experimentation is the best way to get cool new things. Of course that could be just my lack of imagination. :)
I know I am not very math heavy in this answer but that is up to you and experimentation. Just remember that COS and SIN are your friends and there are 360 degrees in a cricle (or 2pi radians depending on your math package).
EDIT: Adding some math for the "Flower"
Starting with a center of (Xo, Yo) and a flower radius of r...
The tips of the petals (P0, P1, etc) are determined by...
X = Xo + (sin((n * pi)/3 + (pi / 6)) * r)
Y = Yo - (cos((n * pi)/3 + (pi / 6)) * r)
where n is the petal number (0..5)
Once you compute a petal tip, just draw the petal and then start a new flower at the tip. You would also set a bounding circle so that any point outside that circle would not be drawn.
I would try to create a PDF with iText in Java. PDF supports vector graphics, so it should scale without problems. I don't know how well iText scales w.r.t. performance when you have a really big number of graphic elements.
A1. You might want to look at turtle-graphics, l-systems, iterated function systems, space filling curves, and probably a lot of other approaches I'm not familiar with or haven't thought of yet.
A2. You can program any of these with any of the languages you suggest. I like Mathematica, but I know that not everyone has a copy of it and I have a copy 'cos I work in number-crunching and get to play with it for making pretty pictures. But Processing, which is free, was designed to be artist-friendly and might be a better starting point for you. Both Mathematica and Processing do the graphics right there and then, no calls to external libraries (or worrying which ones to use).
And, while I agree with everyone who says that vectors are the way to go, don't forget that the final productions step, onto paper or screen, is rendering so give some thought to how that will be done. This might, for example, lead you to Postscript or PDF for an output format.
Have fun
Mark
Well, I used to draw flowers of life with a compass, back then in junior school ... very simple actually ... but I don't think that's the answer you're looking for.
Basically it consists of drawing a circle of the same radius, from every point, until you encounter the big circle (limit).