I don't know much about geometric algorithms, but I just wanted to know if given a mesh, is there an algorithm that outputs a boolean which is true when the mesh is actually a hollow cylinder (like a straight pipe) or else, it will be false. Any reference to the algorithm if it exists would be helpful.
EDIT: I am relatively sure that the body has a surface mesh, basically the only the surfaces of the object is meshed and it uses triangles for meshing.
A key problem is to find the axis of the cylindre. If it is known, you are done. If it is unknown but the cylindre is known to be without holes and delimited by two perpendicular bases, the main axis of inertia can be used. Otherwise, it should be constructible from the coordinates of five points (A Cylinder of Revolution on Five Points http://www.cim.mcgill.ca/~paul/12ICGG6Aw.pdf).
When you know the axis, it suffices to check that the distance of every point to the axis is constant. In case the mesh describes a solid (i.e. a surface with thickness), make sure to pick the points on the outer surface, for example using the orientation of the normals. There will be two distances and the lateral faces must be ignored.
Related
I have two objects: A sphere and an object. Its an object that I created using surface reconstruction - so we do not know the equation of the object. I want to know the intersecting points on the sphere when the object and the sphere intersect. If we had a sphere and a cylinder, we could solve for the equation and figure out the area and all that but the problem here is that the object is not uniform.
Is there a way to find out the intersecting points or area on the sphere?
I'd start by finding the intersection of triangles with the sphere. First find the intersection of each triangle's plane and the sphere, which gives a circle. Then find the circle's intersection/s with the triangle edges in 2D using line/circle tests. The result will be many arcs which I guess you could approximate with lines. I'm not really sure where to go from here without knowing the end goal.
If it's surface area you're after, maybe a numerical approach would be better. I'd cover the sphere in points and count the number inside the non-uniform object. To find if a point is inside, maybe trace outwards and count the intersections with the surface (if it's odd, the point is inside). You could use the stencil buffer for this if you wanted (similar to stencil shadows).
If you want the volume of intersection a quick google search gives "carve", a mesh based CSG library.
Starting with triangles versus the sphere will give you the points of intersection.
You can take the arcs of intersection with each surface and combine them to make fences around the sphere. Ideally your reconstructed object will be in winged-edge format so you could just step from one fence segment to the next, but with reconstructed surfaces I guess you might need to apply some slightly fuzzy logic.
You can determine which side of each fence is inside the reconstructed object and which side is out by factoring in the surface normals along the fence.
You can then cut the sphere along the fences and add the internal bits to the display.
For the other side of things you could remove any triangle completely inside the sphere and cut those that intersect.
I'm trying to implement graphic pipeline in software level. I have some problems with clipping and culling now.
Basically, there are two main concerns:
When should back-face culling take place? Eye coordinate, clipping coordinate or window coordinate? I initially made culling process in eye coordinate, thinking this way could relieve the burden of clipping process since many back-facing vertices have already been discarded. But later I realized that in this way vertices need to take 2 matrix multiplications , namely left multiply model-view matrix --> culling --> left multiply perspective matrix, which increases the overhead to some extent.
How do I do clipping and reconstruct triangle? As far as I know, clipping happens in clipping coordinate(after perspective transformation), in another word homogeneous coordinate in which every vertex is being determined whether no not it should be discarded by comparing its x, y, z components with w component. So far so good, right? But after that I need to reconstruct those triangles which have one or two vertices been discarded. I googled that Liang-Barsky algorithm would be helpful in this case, but in clipping coordinate what clipping plane should I use? Should I just record clipped triangles and reconstruct them in NDC?
Any idea will be helpful. Thanks.
(1)
Back-face culling can occur wherever you want.
On the 3dfx hardware, and probably the other cards that rasterised only, it was implemented in window coordinates. As you say that leaves you processing some vertices you don't ever use but you need to weigh that up against your other costs.
You can also cull in world coordinates; you know the location of the camera so you know a vector from the camera to the face — just go to any of the edge vertices. So you can test the dot product of that against the normal.
When I was implementing a software rasteriser for a z80-based micro I went a step beyond that and transformed the camera into model space. So you get the inverse of the model matrix (which was cheap in this case because they were guaranteed to be orthonormal, so the transpose would do), apply that to the camera and then cull from there. It's still a vector difference and a dot product but if you're using the surface normals only for culling then it saves having to transform each and every one of them for the benefit of the camera. For that particular renderer I was then able to work forward from which faces are visible to determine which vertices are visible and transform only those to window coordinates.
(2)
A variant on Sutherland-Cohen is the thing I remember seeing most often. You'd do a forward scan around the outside of the polygon checking each edge in turn and adjusting appropriately.
So e.g. you start with the convex polygon between points (V1, V2, V3). For each clipping plane in turn you'd do something like:
for(Vn in input vertices)
{
if(Vn is on the good side of the plane)
add Vn to output vertices
if(edge from Vn to Vn+1 intersects plane) // or from Vn to 0 if this is the last edge
{
find point of intersection, I
add I to output vertices
}
}
And repeat for each plane. If you're worried about repeated costs then you either need to adopt a structure with an extra level of indirection between faces and edges or just keep a cache. You'd probably do something like dash round the vertices once marking them as in or out, then cache the point of intersection per edge, looked up via the key (v1, v2). If you've set yourself up with the extra level of indirection then store the result in the edge object.
I'm using Unity, but the solution should be generic.
I will get user input from mouse clicks, which define the vertex list of a closed irregular polygon.
That vertices will define the outer edges of a flat 3D mesh.
To procedurally generate a mesh in Unity, I have to specify all the vertices and how they are connected to form triangles.
So, for convex polygons it's trivial, I'd just make triangles with vertices 1,2,3 then 1,3,4 etc. forming something like a Peacock tail.
But for concave polygons it's not so simple.
Is there an efficient algorithm to find the internal triangles?
You could make use of a constrained Delaunay triangulation (which is not trivial to implement!). Good library implementations are available within Triangle and CGAL, providing efficient O(n*log(n)) implementations.
If the vertex set is small, the ear-clipping algorithm is also a possibility, although it wont necessarily give you a Delaunay triangulation (it will typically produce sub-optimal triangles) and runs in O(n^2). It is pretty easy to implement yourself though.
Since the input vertices exist on a flat plane in 3d space, you could obtain a 2d problem by projecting onto the plane, computing the triangulation in 2d and then applying the same mesh topology to your 3d vertex set.
I've implemented the ear clipping algorithm as follows:
Iterate over the vertices until a convex vertex, v is found
Check whether any point on the polygon lies within the triangle (v-1,v,v+1). If there are, then you need to partition the polygon along the vertices v, and the point which is farthest away from the line (v-1, v+1). Recursively evaluate both partitions.
If the triangle around vertex v contains no other vertices, add the triangle to your output list and remove vertex v, repeat until done.
Notes:
This is inherently a 2D operation even when working on 3D faces. To consider the problem in 2D, simply ignore the vector coordinate of the face's normal which has the largest absolute value. (This is how you "project" the 3D face into 2D coordinates). For example, if the face had normal (0,1,0), you would ignore the y coordinate and work in the x,z plane.
To determine which vertices are convex, you first need to know the polygon's winding. You can determine this by finding the leftmost (smallest x coordinate) vertex in the polygon (break ties by finding the smallest y). Such a vertex is always convex, so the winding of this vertex gives you the winding of the polygon.
You determine winding and/or convexity with the signed triangle area equation. See: http://softsurfer.com/Archive/algorithm_0101/algorithm_0101.htm. Depending on your polygon's winding, all convex triangles with either have positive area (counterclockwise winding), or negative area (clockwise winding).
The point-in-triangle formula is constructed from the signed-triangle-area formula. See: How to determine if a point is in a 2D triangle?.
In step 2 where you need to determine which vertex (v) is farthest away from the line, you can do so by forming the triangles (L0, v, L1), and checking which one has the largest area (absolute value, unless you're assuming a specific winding direction)
This algorithm is not well defined for self-intersecting polygons, and due to the nature of floating point precision, you will likely encounter such a case. Some safeguards can be implemented for stability: - A point should not be considered to be inside your triangle unless it is a concave point. (Such a case indicates self-intersection and you should not partition your set along this vertex). You may encounter a situation where a partition is entirely concave (i.e. it's wound differently to the original polygon's winding). This partition should be discarded.
Because the algorithm is cyclic and involves partitioning the sets, it is highly efficient to use a bidirectional link list structure with an array for storage. You can then partition the sets in 0(1), however the algorithm still has an average O(n^2) runtime. The best case running time is actually a set where you need to partition many times, as this rapidly reduces the number of comparisons.
There is a community script for triangulating concave polygons but I've not personally used it. The author claims it works on 3D points as well as 2D.
One hack I've used in the past if I want to constrain the problem to 2D is to use principal component analysis to find the 2 axes of greatest change in my 3D data and making these my "X" and "Y".
I have an interesting problem that I've been trying to solve for a while. There is no "right" solution to this, as there is no strict criteria for success. What I want to accomplish is a smooth transition between two simple polygons, from polygon A to polygon B. Polygon A is completely contained within polygon B.
My criteria for this transition are:
The transition is continuous in time and space
The area that is being "filled" from polygon A into polygon B should be filled in as if there was a liquid in A that was pouring out into the shape of B
It is important that this animation can be calculated either on the fly, or be defined by a set of parameters that require little space, say less than a few Kb.
Cheating is perfectly fine, any way to solve this so that it looks good is a possible solution.
Solutions I've considered, and mostly ruled out:
Pairing up vertices in A and B and simply interpolate. Will not look good and does not work in the case of concave polygons.
Dividing the area B-A into convex polygons, perhaps a Voronoi diagram, and calculate the discrete states of the polygon by doing a BFS on the smaller convex polygons. Then I interpolate between the discrete states. Note: If polygon B-A is convex, the transition is fairly trivial. I didn't go with this solution because dividing B-A into equally sized small convex polygons was surprisingly difficult
Simulation: Subdivide polygon A. Move each vertex along the polygon line normal (outwards) in discrete but small steps. For each step, check if vertex is still inside B. If not, then move back to previous position. Repeat until A equals B. I don't like this solution because the check to see whether a vertex is inside a polygon is slow.
Does anybody have any different ideas?
If you want to keep this simple and somewhat fast, you could go ahead with your last idea where you consider scaling polygon A so that it gradually fills polygon B. You don't necessarily have to check if the scaled-outward vertices are still inside polygon B. Depending on what your code environment and API is like, you could mask the pixels of the expanding polygon A with the outline of polygon B.
In modern OpenGL, you could do this inside a fragment shader. You would have to render polygon B to a texture, send that texture to the shader, and then use that texture to look up if the current fragment being rendered maps to a texture value that has been set by polygon B. If it is not, the fragment gets discarded. You would need to have the texture be as large as the screen. If not, you would need to include some camera calculations in your shaders so you can "render" the fragment-to-test into the texture in the same way you rendered polygon B into that texture.
I need to create a (large) set of spatial polygons for test purposes. Is there an algorithm that will create a randomly shaped polygon staying within a bounding envelope? I'm using OGC Simple stuff so a routine to create the well known text is the most useful, Language of choice is C# but it's not that important.
Here you can find two examples of how to generate random convex polygons. They both are in Java, but should be easy to rewrite them to C#:
Generate Polygon example from Sun
from JTS mailing list, post Minimum Area bounding box by Michael Bedward
Another possible approach based on generating set of random points and employ Delaunay tessellation.
Generally, problem of generating proper random polygons is not trivial.
Do they really need to be random, or would some real WKT do? Because if it will, just go to http://koordinates.com/ and download a few layers.
What shape is your bounding envelope ? If it's a rectangle, then generate your random polygon as a list of points within [0,1]x[0,1] and scale to the size of your rectangle.
If the envelope is not a rectangle things get a little more tricky. In this case you might get best performance simply by generating points inside the unit square and rejecting any which lie in the part of the unit square which does not scale to the bounding envelope of your choice.
HTH
Mark
Supplement
If you wanted only convex polygons you'd use one of the convex hull algorithms. Since you don't seem to want only convex polygons your suggestion of a circular sweep would work.
But you might find it simpler to sweep along a line parallel to either the x- or y-axis. Assume the x-axis.
Sort the points into x-order.
Select the leftmost (ie first) point. At the y-coordinate of this point draw an imaginary horizontal line across the unit square. Prepare to create a list of points along the boundary of the polygon above the imaginary line, and another list along the boundary below it.
Select the next point. Add it to the upper or lower boundary list as determined by it's y-coordinate.
Continue until you're out of points.
This will generate convex and non-convex polygons, but the non-convexity will be of a fairly limited form. No inlets or twists and turns.
Another Thought
To avoid edge crossings and to avoid a circular sweep after generating your random points inside the unit square you could:
Generate random points inside the unit circle in polar coordinates, ie (r, theta).
Sort the points in theta order.
Transform to cartesian coordinates.
Scale the unit circle to a bounding ellipse of your choice.
Off the top of my head, that seems to work OK