I got an outline (list of points) for a plane I want to generate. The plane is quite big and I need evenly distributed vertices inside the outline. Each vertex has a color value from red to green to visualize some data in the plane. I need to visualize the data as precise as possible in real time.
My idea was to simply create a grid and adjust all the vertices outside of the outline. This turned out to be quite complex.
This is a quick example what I want to achieve.
Is there any algorithm that solves this problem?
Is there another way to generate a mesh from an outline with evenly distributed vertices?
It sounds like you want to do something like this:
1) First generate a triangulate your polygon to create a mesh. There are plenty of options: https://en.wikipedia.org/wiki/Polygon_triangulation
2) Then while any of the edges in the mesh are too long (meaning that the points at either end might be too far apart), add the midpoint of the longest edge to the mesh, dividing the adjacent triangles into 2.
The results is a mesh with every point within a limited distance of other points in every direction. The resulting mesh will not necessarily be optimal, in that it may have more points than are strictly required, but it will probably satisfy your needs.
If you need to reduce the number of points and thin triangles, you can apply Delaunay Triangulation flipping around each candidate edge first: https://en.wikipedia.org/wiki/Delaunay_triangulation#Visual_Delaunay_definition:_Flipping
Although not totally clear from the question, the marching cubes algorithm, adapted to two dimensions, comes to mind. A detailed descriptione of the two-dimensional version can be found here.
Delaunay meshing can create evenly distributed vertices inside a shape. The image below shows a combined grid- and Delaunay-mesh. You may have a look here.
Related
I'm doing 3D Delaunay, with the incremental method. I've tested it in 2D with an initial triangle for inserting the vertices and it works great, but if I use a triangle for 3D, some vertices do not fall into any circumscribed sphere therefore they don't get inserted.
I've tried with a tetrahedron but if the first node falls into the four of the faces, all vertices create new edges towards this new vertex, and deletes all of the initial triangles.
Whichever shape you take, you will always have to deal with side effects.
The best shape is no shape.
This is what we are doing in the CGAL library
http://www.cgal.org
Look at the manual, chapters "2D triangulations" and "3D triangulations".
See also or the journal paper https://hal.inria.fr/inria-00167199/
You can read my answer for this question (Bowyer-Watson algorithm: how to fill "holes" left by removing triangles with super triangle vertices). If the supertriangle is too small sometimes you end with circumcircle outside of the supertriangle. You can try a point-in-polygon test to avoid it.
Given two 3d objects, how can I find if one fits inside the second (and find the location of the object in the container).
The object should be translated and rotated to fit the container - but not modified otherwise.
Additional complications:
The same situation - but look for the best fit solution, even if it's not a proper match (minimize the volume of the object that doesn't fit in the container)
Support for elastic objects - find the best fit while minimizing the "distortion" in the objects
This is a pretty general question - and I don't expect a complete solution.
Any pointers to relevant papers \ articles \ libraries \ tools would be useful
Here is one perhaps less than ideal method.
You could try fixing the position (in 3D space) of 1 shape. Placing the other shape on top of that shape. Then create links that connect one point in shape to a point in the other shape. Then simulate what happens when the links are pulled equally tight. Causing the point that isn't fixed to rotate and translate until it's stable.
If the fit is loose enough, you could use only 3 links (the bare minimum number of links for 3D) and try every possible combination. However, for tighter fit fits, you'll need more links, perhaps enough to place them on every point of the shape with the least number of points. Which means you'll some method to determine how to place the links, which is not trivial.
This seems like quite hard problem. Probable approach is to have some heuristic to suggest transformation and than check is it good one. If transformation moves object only slightly out of interior (e.g. on one part) than make slightly adjust to transformation and test it. If object is 'lot' out (e.g. on same/all axis on both sides) than make new heuristic guess.
Just an general idea for a heuristic. Make a rasterisation of an objects with same pixel size. It can be octree of an object volume. Make connectivity graph between pixels. Check subgraph isomorphism between graphs. If there is a subgraph than that position is for a testing.
This approach also supports 90deg rotation(s).
Some tests can be done even on graphs. If all volume neighbours of a subgraph are in larger graph, than object is in.
In general this is 'refined' boundary box approach.
Another solution is to project equal number of points on both objects and do a least squares best fit on the point sets. The point sets probably will not be ordered the same so iterating between the least squares best fit and a reordering of points so that the points on both objects are close to same order. The equation development for this is a lot of algebra but not conceptually complicated.
Consider one polygon(triangle) in the target object. For this polygon, find the equivalent polygon in the other geometry (source), ie. the length of the sides, angle between the edges, area should all be the same. If there's just one match, find the rigid transform matrix, that alters the vertices that way : X' = M*X. Since X' AND X are known for all the points on the matched polygons, this should be doable with linear algebra.
If you want a one-one mapping between the vertices of the polygon, traverse the edges of the polygons in the same order, and make a lookup table that maps each vertex one one poly to a vertex in another. If you have a half edge data structure of your 3d object that'll simplify this process a great deal.
If you find more than one matching polygon, traverse the source polygon from both the points, and keep matching their neighbouring polygons with the target polygons. Continue until one of them breaks, after which you can do the same steps as the one-match version.
There're more serious solutions that're listed here, but I think the method above will work as well.
What a juicy problem !. As is typical in computational geometry this problem
can be very complicated with a mismatched geometric abstraction. With all kinds of if-else cases etc.
But pick the right abstraction and the solution becomes trivial with few sub-cases.
Compute the Distance Transform of your shapes and Voilà! Your solution is trivial.
Allow me to elaborate.
The distance map of a shape on a grid (pixels) encodes the distance of the closest point on the
shape's border to that pixel. It can be computed in both directions outwards or inwards into the shape.
In this problem, the outward distance map suffices.
Step 1: Compute the distance map of both shapes D_S1, D_S2
Step 2: Subtract the distance maps. Diff = D_S1-D_S2
Step 3: if Diff has only positive values. Then your shapes can be contained in each other(+ve => S1 bigger than S2 -ve => S2 bigger than S1)
If the Diff has both positive and negative values, the shapes intersect.
There you have it. Enjoy !
There is a lot of documentation around how to detect if a marker is within a polygon in Google Maps. However, my question is how can I arbitrarily place a marker inside a polygon (ideally as far as possible from the edges)
I tried calculating the average latitude and longitude of the polygon's points, but this obviously fails in some non-concave polygons.
I also thought about calculating the area's center of mass, but obviously the same happens.
Any ideas? I would like to avoid trial-and-error approaches, even if it works 99% of the time.
There are a few different ways you could approach this, depending on what exactly you're overall goal is.
One approach would be to construct a triangulation of the polygon and place the marker inside one of the triangles. If you're not too worried about optimality you could employ a simple heuristic, like choosing the centroid of the largest triangle, although this obviously wont necessarily give you the point furthest from the polygon edges. There are a number of algorithms for polygon triangulation: ear-clipping or constrained Delaunay triangulation are probably the way to go, and a number of good libraries exist, i.e. CGAL and Triangle.
If you are interested in finding an optimal placement it might be possible to use a skeleton based approach, using either the medial-axis or the straight skeleton of the polygon. The medial-axis is the set of curves equi-distant from the polygon edges, while the straight skeleton is a related structure. Specifically, these type of structures can be used to find points which are furthest away from the edges, check this out for a label placement application for GIS using an approach based on the straight skeleton.
Hope this helps.
I haven't done much research on this yet, but i'm just asking around in case this has been done before.
Here's my problem:
I have a set of cubes of an arbitrary height, width and depth. These are either filled or empty. What i'm looking to do is develop an algorithm that is going to create an optimal mesh for this set of cubes by combining the faces of neighboring cubes into one.
My current idea is to step through the set 6 times(twice along each axis, once forwards and once back), and look at the set in cross section. Ignoring cubes that won't be visible from the outside, i'd like to build polygonal face for those cubes in that section. At the end of this, i should have (x+y+z)*2 of these faces. Combining them should give me the resulting optimized mesh for the voxel set.
I'm stumped on the triangulation process however.
If you want to create a mesh from voxel data, the most commonly used algorithm is marching cubes. However I suggest you search the net for iso-surface extraction for more advanced methods.
Starting with a 3D mesh, how would you give a rounded appearance to the edges and corners between the polygons of that mesh?
Without wishing to discourage other approaches, here's how I'm currently approaching the problem:
Given the mesh for a regular polyhedron, I can give the mesh's edges a rounded appearance by scaling each polygon along its plane and connecting the edges using cylinder segments such that each cylinder is tangent to each polygon where it meets that polygon.
Here's an example involving a cube:
Here's the cube after scaling its polygons:
Here's the cube after connecting the polygons' edges using cylinders:
What I'm having trouble with is figuring out how to deal with the corners between polygons, especially in cases where more than three edges meet at each corner. I'd also like an algorithm that works for all closed polyhedra instead of just those that are regular.
I post this as an answer because I can't put images into comments.
Sattle point
Here's an image of two brothers camping:
They placed their simple tents right beside each other in the middle of a steep walley (that's one bad place for tents, but thats not the point), so one end of each tent points upwards. At the point where the four squares meet you have a sattle point. The two edges on top of each tent can be rounded normally as well as the two downward edges. But at the sattle point you have different curvature in both directions and therefore its not possible to use a sphere. This rules out Svante's solution.
Selfintersection
The following image shows some 3D polygons if viewed from the side. Its some sharp thing with a hole drilled into it from the other side. The left image shows it before, the right after rounding.
.
The mass thats get removed from the sharp edge containts the end of the drill hole.
There is someething else to see here. The drill holes sides might be very large polygons (lets say it's not a hole but a slit). Still you only get small radii at the top. you can't just scale your polygons, you have to take into account the neighboring polygon.
Convexity
You say you're only removing mass, this is only true if your geometry is convex. Look at the image you posted. But now assume that the viewer is inside the volume. The radii turn away from you and therefore add mass.
NURBS
I'm not a nurbs specialist my self. But the constraints would look something like this:
The corners of the nurbs patch must be at the same position as the corners of the scaled-down polygons. The normal vectors of the nurb surface at the corners must be equal to the normal of the polygon. This should be sufficent to gurarantee that the nurb edge will be a straight line following the polygon edge. The normals also ensure that no visible edges will result at the border between polygon and nurbs patch.
I'd just do the math myself. nurbs are just polygons. You'll have some unknown coefficients and your constraints. This gives you a system of equations (often linear) that you can solve.
Is there any upper bound on the number of faces, that meet at that corner?
You might you might employ concepts from CAGD, especially Non-Uniform Rational B-Splines (NURBS) might be of interest for you.
Your current approach - glueing some fixed geometrical primitives might be too inflexible to solve the problem. NURBS require some mathematical work to get used to, but might be more suitable for your needs.
Extrapolating your cylinder-edge approach, the corners should be spheres, resp. sphere segments, that have the same radius as the cylinders meeting there and the centre at the intersection of the cylinders' axes.
Here we have a single C++ header for generating triangulated rounded 3D boxes. The code is in C++ but also easy to transplant to other coding languages. Also it's easy to be modified for other primitives like quads.
https://github.com/nepluno/RoundCornerBox
As #Raymond suggests, I also think that the nepluno repo provides a very good implementation to solve this issue; efficient and simple.
To complete his answer, I just wrote a solution to this issue in JS, based on the BabylonJS 3D engine. This solution can be found here, and can be quite easily replaced by another 3D engine:
https://playground.babylonjs.com/#AY7B23