We have a 3d triangulated surface and there is a point on it. How can i find the triangle which contains the point.
We can find with testing all triangles but it is slow way. I must make the algorithm faster.
Is there any searching algorithm or is there any technique about reducing searching area?
What you need is a spatial data structure, they allow those typical queries of computational geometry. Your point is a query point for the set of triangles.
You might for example calculate a minimal bounding box for each triangle and store those into a R-tree (keep a map of which mbb is for what triangle or put those triangles as leave nodes in the R-tree) and then fast lookups of the best bounding box should give you maybe not the final result, but I think it would deliver a much reduced search area (a list of matching mbbs which result in a list of triangle candidates), where you then quickly search the exact triangle (because bounding boxes and triangles differ a bit).
You could speed up the comparison by using the bounding box of each triangle to test if the point is not contained.
Use a ´bounding sphere collision detection´ algorithm to test all triangles.
This algorithm is fast, but has false positives. On all triangles that fulfill the bounding sphere test, use your algorithm to do the final collision test.
Related
I am trying to write a Rigid body simulator, and during simulation, I am not only interested in finding whether two objects collide or not, but also the point as well as normal of collision. I have found lots of resources which actually says whether two OBB are colliding or not using separating axis theorem. Also I am interested in 3D representation of OBB. Now, if I know the axis with minimum overlap region for two colliding OBB, is there any way to find the point of collision and normal of collision? Also, there are two major cases of collision, first, point-face and second edge-edge.
I tried to google this problem, but almost every solution is only detecting collision with true or false.
Kindly somebody help!
Look at the scene in the direction of the motion (in other terms, apply a change of coordinates such that this direction becomes vertical, and drop the altitude). You get a 2D figure.
Considering the faces of the two boxes that face each other, you will see two hexagons each split in three parallelograms.
Then
Detect the intersections between the edges in 2D. From the section ratios along the edges, you can determine the actual z distances.
For all vertices, determine the face they fall on in the other box; and from the 3D equations, the piercing point of the viewing line into the face plane, hence the distance. (Repeat this for the vertices of A and B.)
Comparing the distances will tell you which collision happens first and give you the coordinates of the first meeting point (in the transformed system, the back to absolute coordinates).
The point-in-face problem is easy to implement as the facesare convex polygons.
I am working on small project that requires me to quickly find which triangles within a set of triangles is either partially or entirely contained within a given rectangular region. I am interested in optimizing for fast searches - I am not memory limited.
This is not an area I am too familiar with, so all I've been able to do thus far is to poke around on Google for standard algorithms for dealing with this problem. The closest I've gotten to so far is to use two interval trees. This is a bit clumsy, since I have to perform a test for interval overlap between the edges of each triangle and the edges of the rectangular region in both directions x and y.
Can someone point me to any resource where the 'correct' way of dealing with this problem is?
Thanks!
Edit: I forgot to mention that the rectangular regions I am currently using are parallel to the coordinate axes x and y. For the time being, I am happy with any solution that exploits this constraint. Generally, though, a solution with completely arbitrary rectangles would be great to know about.
You can use an AABBTree (AABB stands for Axis Aligned Bounding Box tree), the
idea is to enclose each triangle in its axis aligned bounding box, then build a tree that has the initial triangles as leafs, and where upper nodes have a bounding box that is the union of the bounding boxes of its children. Then when searching which triangles have a non-empty intersection with "something", you check whether the "something" has an intersection with the bounding box of a node, and go down the tree to test its children when it's the case (recursive function).
You can find efficient implementations of AABBTrees in:
CGAL: http://doc.cgal.org/latest/AABB_tree/
the GEOGRAM library that I am writing: http://alice.loria.fr/software/geogram/doc/html/classGEO_1_1MeshFacetsAABB.html
OpCode: http://www.codercorner.com/Opcode.htm
Assuming the rectangle is axis aligned, I'd do this:
Compare the bounding box of a triangle to the region. If it is inside, the triangle is inside. If there is no overlap at all, it's not. Use an interval tree for each dimension for this step if you need to check the same set of triangles with different regions.
We have checked the two simple cases in step one, so we know the region and bounding box overlap. Check if any of the points of the triangle is inside the rectangle. If so, the triangle is inside.
Check the four sides of the rectangle with the three sides of the triangle for line segment intersections
If no preprocessing of the set of triangles is allowed, there is nothing better you can do than comparing exhaustively every triangle to the window.
To solve the triangle/rectangle overlap problem easily (or just to reason about it), you can form the Minkowski sum of the two polygons, to turn the problem in a "point-in-convex-polygon" instance.
Of course, an initial axis-aligned bounding box test is welcome.
If your window is a rotated rectangle, you can "unrotate" the whole scene to make the window axis-aligned and revert to the first problem.
I'm working on a problem which is to find the closest trio of neighbours for a set of arbitrarily placed non-intersecting ellipses. As a new user I'm not allowed to include image tags but I've included the URL at the bottom of the page as I always think I'm better able to explain myself with visual aids. The picture demonstrates what I mean with Apollonius circles connecting the 3 nearest ellipses to one another.
So far I have tried using the minimum distances between the ellipses and modifying Delaunay Triangulation, via incremental and sweepline methods, used various techniques involving in circles of triangles formed between every 3 ellipse configuration etc, and attempted to estimate the neighbours with bounding boxes, and have completely run out of ideas of how to actually get this working efficiently
Although I have worked out a solution it involves exhaustively searching and comparing every trio of ellipses with every other ellipse and has a time complexity of n(n-1)(n-2)/3!. And on top of that each calculation is done iteratively rather than algebraically.
Would anyone even have an idea of how to go about this that could be done algebraically and at lower then a n^2 time complexity?
Even a suggestion of a technique would suit me to have a try at, because right now I've been working at it for nearly 3 weeks and really am no closer to a decent answer.
If you calculate a Voronoi diagram for your ellipses, then your circles center points would be placed at the intersection of the diagrams.
http://ima.umn.edu/nuggets/voronoi.html
You could pack your ellipses into an R-tree based on their bounding boxes. The R-tree is a binary-tree-like data structure for spatial objects, and supports efficient nearest-neighbour and kth nearsest neighbour queries via traversal.
For large data sets with many ellipses, use of an R-tree should reduce the number of distance tests significantly, only scanning a subset of the tree in the neighbourhood of the query.
Hope this helps.
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
What is a fast algorithm for determining whether or not a point is inside a 3D mesh? For simplicity you can assume the mesh is all triangles and has no holes.
What I know so far is that one popular way of determining whether or not a ray has crossed a mesh is to count the number of ray/triangle intersections. It has to be fast because I am using it for a haptic medical simulation. So I cannot test all of the triangles for ray intersection. I need some kind of hashing or tree data structure to store the triangles in to help determine which triangle are relevant.
Also, I know that if I have any arbitrary 2D projection of the vertices, a simple point/triangle intersection test is all necessary. However, I'd still need to know which triangles are relevant and, in addition, which triangles lie in front of a the point and only test those triangles.
I solved my own problem. Basically, I take an arbitrary 2D projection (throw out one of the coordinates), and hash the AABBs (Axis Aligned Bounding Boxes) of the triangles to a 2D array. (A set of 3D cubes as mentioned by titus is overkill, as it only gives you a constant factor speedup.) Use the 2D array and the 2D projection of the point you are testing to get a small set of triangles, which you do a 3D ray/triangle intersection test on (see Intersections of Rays, Segments, Planes and Triangles in 3D) and count the number of triangles the ray intersection where the z-coordinate (the coordinate thrown out) is greater than the z-coordinate of the point. An even number of intersections means it is outside the mesh. An odd number of intersections means it is inside the mesh. This method is not only fast, but very easy to implement (which is exactly what I was looking for).
This is algorithm is efficient only if you have many queries to justify the time for constructing the data structure.
Divide the space into cubes of equal size (we'll figure out the size later). For each cube know which triangles has at least a point in it. Discard the cubes that don't contain anything. Do a ray casting algorithm as presented on wikipedia, but instead o testing if the line intersects each triangle, get all the cubes that intersect with the line, and then do ray casting only with the triangles in these cubes. Watch out not to test the same triangle more than one time because it is present in two cubes.
Finding the proper cube size is tricky, it shouldn't be neither to big or too small. It can only be found by trial and error.
Let's say number of cubes is c and number of triangles is t.
The mean number of triangles in a cube is t/c
k is mean number of cubes that intersect the ray
line-cube intersections + line-triangle intersection in those cubes has to be minimal
c+k*t/c=minimal => c=sqrt(t*k)
You'll have to test out values for the size of the cubes until c=sqrt(t*k) is true
A good starting guess for the size of the cube would be sqrt(mesh width)
To have some perspective, for 1M triangles you'll test on the order of 1k intersections
Ray Triangle Intersection appears to be a good algorithm when it comes to accuracy. The Wiki has some more algorithms. I am linking it here, but you might have seen this already.
Can you, perhaps improvise by, maintaining a matrix of relationship between the points and the plane to which they make the vertices? This subject appears to be a topic of investigation in the academia. Not sure how to access more discussions related to this.