I have problem of packing 2 arbitrary polygons. I.e. we have 2 arbitrary polygons. We are to find such placement of this polygons (we could make rotations and movements), when rectangle, which circumscribes this polygons has minimal area.
I know, that this is a NP-complete problem. I want to choose an efficient algorithm for solving this problem. I' looking for No-Fit-Polygon approach. But I could't find anywhere the simple and clear algorithm for finding the NFP of two arbitrary polygons.
The parameter space does not seem too big and testing it is not too bad either. If you fix one polygon, the other ploygon can be shifted along x-axis by X, and shifted along y-axis by Y and rotated by r.
The interesting region for X and Y can be determined by finding some bounding box for for the polygons. r of course is between and 360 degrees.
So how about you tried a set of a set of equally spaced intervals in the interesting range for X,Y and r. Perhaps, once you found the interesting points in these dimensions, you can do more finer grained search.
If its NP-complete then you need heuristics, not algorithms. I'd try putting each possible pair of sides together and then sliding one against the other to minimise area, constrained by possible overlap if they are concave of course.
There is an implementation of a robust and comprehensive no-fit polygon generation in a C++ library using an orbiting approach: https://github.com/kallaballa/libnfporb
(I am the author of libnfporb)
Related
I am trying to develop an algorithm that performs the following :
Given a 2D polygon and a 3D polyhedron, determine if the 2D polygon is a projection of the 3D polyhedron (a perspective projection to be precise) without knowing which transformation matrix we may have possibly used for the projection.
input
{2D Polygon}
{3D Polyhedron}
output
{bool} whether or not it's a perspective projection
I am not asking for code, but I would simply like to know if this is feasible in polynomial time.
Any help will be greatly appreciated.
A 3D to 2D perspective projection has 7 degrees of freedom (6 for the relative motion of the scene with respect to the camera, 1 for the focal length).
Select four vertices in the 2D projection and consider all possible correspondences with polyhedron vertices (there is a polynomial number of such associations). Then form a system of 7 equations in the 7 unknown parameters (unfortunately a nonlinear one; maybe the eighth equation can be useful to select among multiple solutions).
Knowing the parameters, you can check a solution by re-projecting the polyhedron and comparing to the polygon (with further search for correspondences with vertices and edges).
All of this will take polynomial time (quartic if I am right), if one admits that the solver takes bounded time (hence bounded precision).
If the focal length is known, then a better approach is possible. Indeed, with only 6 unknowns, you can find the projection parameters from the projection of just three points. This problem is known to have an analytical solution (actually up to 4 of them), as described at length in "New Algorithms for the Perspective-Three-Point Problem, GAO Xiaoshan & CHEN Hangfei, Vol.16 No.3 J. Comput. Sci. & Technol."
This should lead to an O(N³) exact procedure.
More generally speaking, you form putative correspondences between N pairs of points, solve the corresponding Perspective-N-point problem, and check the hypothesis by reprojecting the polyhedron and comparing to the known projection to validate the hypothesis.
Just an idea for an algorithm:
Take a triangle of the projection made of three points next to each other not on the same line. Iterate through all corresponding triangles of the original. For all possible projections that solve the pair of triangles, check if the rest matches.
I must admit I am not sure right now if there could be infinite solutions for triangles (which would be hard to iterate)? If so, start with four points.
I think it is possible but you have to do a fair amount of reverse engineering. A 2D sketch that represents a 3D object is known as an Orthographic Projection. The link shows you the transformation matrices you need apply to transform the 3D point onto its 2D projection. Now, how do you go the opposite way? Inverse matrices with a mix of some inverse transformations (translation, scaling, rotation...)? I think this is a good lead to follow.
Given a general polygon P (might contain holes) and a number of rectangles K, I want to find K rectangles {r1,r2,...,rk} such that the polygon is contained in the union of these rectangles. Also, I want to minimize the area of the union of the rectangles.
Note that the rectangles may overlap each other. Also, they're axis-aligned.
As an example, for K=1, the algorithm should report the minimum bounding box.
I've read some papers on the subject, namely "A linear-time approximation algorithm for minimum rectangular covering" and "Covering a polygonal region by rectangles".
The first article approaches the problem by dividing the polygon into simpler polygons, but it considers only rectangles that are within the polygon.
The second article assumes that the rectangles dimension are fixed and simply translates them around and tries to cover the polygon with the minimum number of rectangles.
I know that there is an article named "covering a polygon with few rectangles" that has what I believe to be exactly what I'm looking for, but I would have to pay in order to have access to it and I'd like to dig depeer before spending any money.
Since this is an np-complete or np-hard (not sure) problem, i'm not expecting fast exact algorithms, therefore approximations are also relevant.
Does anyone have knowledge of previous work on this particular problem? Any work that might relate is also welcome. Also, if you have ideas on how to address the problem it would be great!
Thanks in advance.
What is the best method to detect whether the red rectangle overlaps the black polygon? Please refer to this image:
There are four cases.
Rect is outside of Poly
Rect intersects Poly
Rect is inside of Poly
Poly is inside of Rect
First: check an arbitrary point in your Rect against the Poly (see Point in Polygon). If it's inside you are done, because it's either case 3 or 2.
If it's outside case 3 is ruled out.
Second: check an arbitrary point of your Poly against the Rect to validate/rule out case 4.
Third: check the lines of your Rect against the Poly for intersection to validate/rule out case 2.
This should also work for Polygon vs. Polygon (convex and concave) but this way it's more readable.
If your polygon is not convex, you can use tessellation to subdivide it into convex subparts. Since you are looking for methods to detect a possible collision, I think you could have a look at the GJK algorithm too. Even if you do not need something that powerful (it provides information on the minimum distance between two convex shapes and the associated witness points), it could prove to be useful if you decide to handle more different convex shapes.
Christer Ericson made a nice Powerpoint presentation if you want to know more about this algorithm. You could also take a look at his book, Real-Time Collision Detection, which is both complete and accessible for anyone discovering collision detection algorithms.
If you know for a fact that the red rectangle is always axis-aligned and that the black region consists of several axis-aligned rectangles (I'm not sure if this is just a coincidence or if it's inherent to the problem), then you can use the rectangle-on-rectangle intersection algorithm to very efficiently compute whether the two shapes overlap and, if so, where they overlap.
If you use axis-aligned rectangles and polygons consist of rectangles only, templatetypedef's answer is what you need.
If you use arbitrary polygons, it's a much more complex problem.
First, you need to subdivide polygons into convex parts, then perform collision detection using, for example, the SAT algorithm
Simply to find whether there is an intersection, I think you may be able to combine two algorithms.
1) The ray casting algorithm. Using the vertices of each polygon, determine if one of the vertices is in the other. Assuming you aren't worried about the actual intersection region, but just the existence of it. http://en.wikipedia.org/wiki/Point_in_polygon
2) Line intersection. If step 1 produces nothing, check line intersection.
I'm not certain this is 100% correct or optimal.
If you actually need to determine the region of the intersection, that is more complex, see previous SO answer:
A simple algorithm for polygon intersection
I've been searching far and wide on the seven internets, and have come to no avail. The closest to what I need seems to be The cutting stock problem, only in 2D (which is disappointing since Wikipedia doesn't provide any directions on how to solve that one). Another look-alike problem would be UV unwrapping. There are solutions there, but only those that you get from add-ons on various 3D software.
Cutting the long talk short - what I want is this: given a rectangle of known width and height, I have to find out how many shapes (polygons) of known sizes (which may be rotated at will) may I fit inside that rectangle.
For example, I could choose a T-shaped piece and in the same rectangle I could pack it both in an efficient way, resulting in 4 shapes per rectangle
as well as tiling them based on their bounding boxes, case in which I could only fit 3
But of course, this is only an example... and I don't think it would be much use to solving on this particular case. The only approaches I can think of right now are either like backtracking in their complexity or solve only particular cases of this problem. So... any ideas?
Anybody up for a game of Tetris (a subset of your problem)?
This is known as the packing problem. Without knowing what kind of shapes you are likely to face ahead of time, it can be very difficult if not impossible to come up with an algorithm that will give you the best answer. More than likely unless your polygons are "nice" polygons (circles, squares, equilateral triangles, etc.) you will probably have to settle for a heuristic that gives you the approximate best solution most of the time.
One general heuristic (though far from optimal depending on the shape of the input polygon) would be to simplify the problem by drawing a rectangle around the polygon so that the rectangle would be just big enough to cover the polygon. (As an example in the diagram below we draw a red rectangle around a blue polygon.)
Once we have done this, we can then take that rectangle and try to fit as many of that rectangle into the large rectangle as possible. This simplfies the problem into a rectangle packing problem which is easier to solve and wrap your head around. An example of an algorithm for this is at the following link:
An Effective Recursive Partitioning Approach for the Packing of Identical Rectangles in a Rectangle.
Now obviously this heuristic is not optimal when the polygon in question is not close to being the same shape as a rectangle, but it does give you a minimum baseline to work with especially if you don't have much knowledge of what your polygon will look like (or there is high variance in what the polygon will look like). Using this algorithm, it would fill up a large rectangle like so:
Here is the same image without the intermediate rectangles:
For the case of these T-shaped polygons, the heuristic is not the best it could be (in fact it may be almost a worst case scenario for this proposed approximation), but it would work very well for other types of polygons.
consider what the other answer said by placing the t's into a square, but instead of just leaving it as a square set the shapes up in a list. Then use True and False to fill the nested list as the shape i.e. [[True,True,True],[False,True,False]] for your T shape. Then use a function to place the shapes on the grid. To optimize the results, create a tracker which will pay attention to how many false in a new shape overlap with trues that are already on the grid from previous shapes. The function will place the shape in the place with the most overlaps. There will have to be modifications to create higher and higher optimizations, but that is the general premise which you are looking for.
I'm looking for a packing algorithm which will reduce an irregular polygon into rectangles and right triangles. The algorithm should attempt to use as few such shapes as possible and should be relatively easy to implement (given the difficulty of the challenge). It should also prefer rectangles over triangles where possible.
If possible, the answer to this question should explain the general heuristics used in the suggested algorithm.
This should run in deterministic time for irregular polygons with less than 100 vertices.
The goal is to produce a "sensible" breakdown of the irregular polygon for a layman.
The first heuristic applied to the solution will determine if the polygon is regular or irregular. In the case of a regular polygon, we will use the approach outlined in my similar post about regular polys: Efficient Packing Algorithm for Regular Polygons
alt text http://img401.imageshack.us/img401/6551/samplebj.jpg
I don't know if this would give the optimal answer, but it would at least give an answer:
Compute a Delaunay triangulation for the given polygon. There are standard algorithms to do this which will run very quickly for 100 vertices or fewer (see, for example, this library here.) Using a Delaunay triangulation should ensure that you don't have too many long, thin triangles.
Divide any non-right triangles into two right triangles by dropping an altitude from the largest angle to the opposite side.
Search for triangles that you can combine into rectangles: any two congruent right triangles (not mirror images) which share a hypotenuse. I suspect there won't be too many of these in the general case unless your irregular polygon had a lot of right angles to begin with.
I realize that's a lot of detail to fill in, but I think starting with a Delaunay triangulation is probably the way to go. Delaunay triangulations in the plane can be computed efficiently and they generally look quite "natural".
EDITED TO ADD: since we're in ad-hoc heuristicville, in addition to the greedy algorithms being discussed in other answers you should also consider some kind of divide and conquer strategy. If the shape is non-convex like your example, divide it into convex shapes by repeatedly cutting from a reflex vertex to another vertex in a way that comes as close to bisecting the reflex angle as possible. Once you've divided the shape into convex pieces, I'd consider next dividing the convex pieces into pieces with nice "bases", pieces with at least one side having two acute or right angles at its ends. If any piece doesn't have such a "base" you should be able to divide it in two along a diameter of the piece, and get two new pieces which each have a "base" (I think). This should reduce the problem to dealing with convex polygons which are kinda-sorta trapezoidal, and from there a greedy algorithm should do well. I think this algorithm will subdivide the original shape in a fairly natural way until you get to the kinda-sorta trapezoidal pieces.
I wish I had time to play with this, because it sounds like a really fun problem!
My first thought (from looking at your diagram above) would be to look for 2 adjacent right angles turning the same direction. I'm sure that won't catch every case where a rectangle will help, but from a user's point of view, it's an obvious case (square corners on the outside = this ought to be a rectangle).
Once you've found an adjacent pair of right angles, take the length of the shorter leg, and there's one rectangle. Subtract this from the polygon left to tile, and repeat. When there's no more obvious external rectangles to remove, then do your normal tiling thing (Peter's answer sounds great) on that.
Disclaimer: I'm no expert on this, and I haven't even tried it...