delaunay

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 2 years ago . For the purposes of implementing a high-performance dynamic pathfinding algorithm on a sphere (in C++), I'm interested in performing an incremental constrained delaunay triangulation on the surface of a sphere. Existing libraries do not seem to be sufficient - the closest that I've been able to find so far is

问题 Given a set of points in the plane, a notion of alpha-shape, for a given positive number alpha, is defined by finding the Delaunay triangulation and deleting any triangles for which at least one edge exceeds alpha in length. Here's an example using d3: http://bl.ocks.org/gka/1552725 The problem is that, when there are thousands of points, simply drawing all the interior triangles is too slow for an interactive visualization, so I'd like to just find the bounding polygons. This isn't so simple

问题 Problem: Mesh generation from 3D points (with x, y and z coordinates). What I have is points in 3D space (with x, y and z coordinates) you can see it in image 1. What would be the output is image 2 or image 3, or image 4. In short it would be mesh. Material on it can be provided if I have the mesh. I have seen many people say about Delaunay triangulations or constrained Delaunay triangulations will help me in mesh generation, but what I mostly found is its implementation in 2D points (with

问题 Problem: Mesh generation from 3D points (with x, y and z coordinates). What I have is points in 3D space (with x, y and z coordinates) you can see it in image 1. What would be the output is image 2 or image 3, or image 4. In short it would be mesh. Material on it can be provided if I have the mesh. I have seen many people say about Delaunay triangulations or constrained Delaunay triangulations will help me in mesh generation, but what I mostly found is its implementation in 2D points (with

问题 I have a scipy.spatial.Delaunay() triangulation, and I would like to remove 'skinny' triangles from it. How would I do this? I have seen that some other software (like here: Houdini triangulate2d) have a 'minimum angle' option precisely to prevent skinny triangles. Is there some equivalent option for scipy.spatial.Delaunay() , or can someone provide a simple algorithm? 来源： https://stackoverflow.com/questions/56136510/how-to-remove-skinny-triangles-in-scipy-spatial-delaunay

问题 I'm trying to implement the Delaunay triangulation in C++. Currently it's working, but I'm not getting the correct amount of triangles. I try it with 4 points in a square pattern : (0,0), (1,0), (0,1), (1,1). Here's the algorithm I use : std::vector<Triangle> Delaunay::triangulate(std::vector<Vec2f> &vertices) { // Determinate the super triangle float minX = vertices[0].getX(); float minY = vertices[0].getY(); float maxX = minX; float maxY = minY; for(std::size_t i = 0; i < vertices.size(); +

问题 How to remove distances from Delaunay triangulation which are larger than I need? Example data: x<-rep(1:12, c(2,2,7,9,10,5,4,6,10,10,9,4)) y<-c(1,2,1,2,1:3,5:8,1:9,1:10,2,7:10,8:11,7:12,3:12,3:12,4:12,5,8:10) x_plus<-seq(0.2:0.8, by=0.1) x<-x+sample(x_plus, 78, replace=TRUE) y<-y+sample(x_plus, 78, replace=TRUE) Plotting the map: plot(x,y) Delaunay triangulation with tri.mesh() - package(tripack) my.triangles<-tri.mesh(x,y) plot(my.triangles, do.points=FALSE, lwd=0.2) points(x,y, col = "blue

问题 I would like to make a color plot in MATLAB similar to this plot: I have managed to create all the points [x,y] needed in order to create the vertexes and I have a map with colors of each vertex, so I can get the following. But I do not get it how to make the axis work. Code so far: % Equilateral grid tcorner = [0.0, 0.5, 1.0; 0.0, 1.0*sqrt(3)/2, 0.0]; tg = triangle_grid( 1/0.05, tcorner ); tgx = tg(1,:); tgy = tg(2,:); % Create triangles tri = delaunay(tgx,tgy); % Plot h = trisurf(tri, tgx,

问题 I have this matrix: v <- rbind( c( -5, -5, 16 ) , c( -5, 8, 3 ) , c( 4, -1, 3 ) , c( 4, -5, 7 ) , c( 4, -1, -10 ) , c( 4, -5, -10 ) , c( -5, 8, -10 ) , c( -5, -5, -10 )) And then: > library(DatabionicSwarm) Delaunay4Points(v, IsToroid = FALSE) Error in UniqDelaunay[Uniq2DataInd, Uniq2DataInd] : subscript out of bounds Is it a bug ? Unfortunately there's no Github repo to fill an issue. I've compared with another tool and the result should be: [0,1,0,1,0,0,0,1] [1,0,1,1,0,0,1,1] [0,1,0,1,1,1,1

问题 I am trying to triangulate an annulus using the scipy.spatial.Delaunay() function, but cannot get the desired result. Here is my code: from scipy.spatial import Delaunay NTheta = 26 NR = 8 a0 = 1.0 #define base rectangle (r,theta) = (u,v) u=np.linspace(0, 2*np.pi, NTheta) v=np.linspace(1*a0, 3*a0, NR) u,v=np.meshgrid(u,v) u=u.flatten() v=v.flatten() #evaluate the parameterization at the flattened u and v x=v*np.cos(u) y=v*np.sin(u) #define 2D points, as input data for the Delaunay