thanks for reading this question. My title is basically what I'm trying to achieve. I did a poisson surface mesh generation using Poisson_surface_reconstruction_3(cgal). I can't figure out how to map the node identities of my resulting surface mesh into my starting point sets?
The output of my poisson surface generation is produced by the following lines:
CGAL::facets_in_complex_2_to_triangle_mesh(c2t3, output_mesh);
out << output_mesh;
In my output file, there are some x y z coordinates, followed by a set of 3 integers each line, I think they indicates which nodes form a delaunay triangle. The problem is that the output points do not correspond to my initial point set, since not any x y z value match to any of my original points. Yet I'm trying to figure out which points are forming a delaunay triangles in my original point set.
Could someone suggest me how can I do this in cgal?
Many thanks.
The poisson recontruction algorithm consist in meshing an implicit function that somehow fits you input points. In practice, it means that you input point will no belong to the set of points of the output surface, and won't even lie exactly on triangles of the output surface. However, they should not be too far from the output surface (except if you have some really sparse sampling parts).
What you can do to locate your input points with the output surface is to use the function closest_point_and_primitive() from the AABB-tree class.
Here is an example of how to build the tree from a mesh.
Related
My source data consists of a set of (x,y,z,e) samples. It can be visualized like this, where the dots are the (x,y,z) samples in 3D space and the color reflects the e value. The (x,y,z) samples compose a surface.
I want a kind of interpolation method, that I can feed with random (x,y,z) coordinates that are close to the surface and that can output an interpolated e value.
I tried the scipy LinearNDInterpolator. It works fine, but only for input (x,y,z) points that lie inside the convex hull of the surface. When the input is only slightly outside, the interpolator returns 'nan'.
I'm a bit out of ideas how to solve this.
I can only think of iterating the each line in the grid to find the points closest to the random (x,y,z) input and do linear interpolations from these points. But if I could somehow reconstruct the surface, that would be more accurate.
I use contract_until_convergence function from CGAL Mean_curvature_flow_skeletonization to produce skeleton from input polygon.
https://doc.cgal.org/latest/Surface_mesh_skeletonization/classCGAL_1_1Mean__curvature__flow__skeletonization.html
In some cases the skeleton creates branches (see top of the image above, skeleton in red color) that does not exist in input polygon. Is there some parameters to set to prevent this ?
using Skeletonization = CGAL::Mean_curvature_flow_skeletonization<Polyhedron>;
Skeletonization mean_curve_skeletonizer(polyhedron);
mean_curve_skeletonizer.contract_until_convergence();
There are two parameters controlling the quality of the skeleton:
quality_speed_tradeoff()
medially_centered_speed_tradeoff()
Also one thing that affect the skeleton is the sampling of the input surface that is used to compute Voronoi poles. In the original papers, it is said: Given a sufficiently good sampling, the Voronoi poles [ACK00] form a provably convergent sampling of the medial axis.
[ACK00]AMENTAN., CHOIS., KOLLURIR. K.: The powercrust, unions of balls, and the medial axis transform. Computational Geometry: Theory and Applications 19(2000), 127–153.3
You can use the function isotropic_remeshing with a sufficiently small target edge length to improve the Voronoi pole computation.
I have a point cloud (data set) (3D) representing an urban terrain consisting of flat roof surfaces (of buildings) . My aim is to figure out the flat surfaces , waterbodies from the given data set .The data set is a text file consisting of the number of points followed by their individual x , y , z co-ordinates. As a trial attempt , I have generated the 2D-Delaunay triangulation of the given data set to get the triangulated surface. Henceforth, I plan to execute a graph-traversal over the faces of the triangulation to look for neighbourhood points with nearly the same z-coordinate value and treat them as a flat surface . I am using CGAL libraries to accomplish the same in C++. Is there a better approach for identifying flat surface or my method seems decent enough ?
You might get inspiration (or just run) Advancing Front Reconstruction
No idea how your point cloud looks, but maybe Point Set Shape Detection
might help you to identify flat areas.
I have a 3D model, which consists of the 3D triangular meshes. I want to partition the meshes into different groups. Each group represents a surface, such as a planar face, cylindrical surface. This is something like surface recognition/reconstruction.
The input is a set of 3D triangular meshes. The output is the mesh segmentations per surface.
Is there any library meets my requirement?
If you want to go into lots of mesh processing, then the point cloud library is a good idea, but I'd also suggest CGAL: http://www.cgal.org for more algorithms and loads of structures aimed at meshes.
Lastly, the problem you describe is most easily solved on your own:
enumerate all vertices
enumerate all polygons
create an array of ints with the size of the number of vertices in your "big" mesh, initialize with 0.
create an array of ints with the size of the number of polygons in your "big" mesh, initialize with 0.
initialize a counter to 0
for each polygon in your mesh, look at its vertices and the value that each has in the array.
if the values for each vertex are zero, increase counter and assign to each of the values in the vertex array and polygon array correspondingly.
if not, relabel all vertices and polygons with a higher number to the smallest, non-zero number.
The relabeling can be done quickly with a look up table.
This might save you lots of issues interfacing your code to some library you're not really interested in.
You should have a look at the PCL library, it has all these features and much more: http://pointclouds.org/
I'm trying to implement a geometry templating engine. One of the parts is taking a prototypical polygonal mesh and aligning an instantiation with some points in the larger object.
So, the problem is this: given 3d point positions for some (perhaps all) of the verts in a polygonal mesh, find a scaled rotation that minimizes the difference between the transformed verts and the given point positions. I also have a centerpoint that can remain fixed, if that helps. The correspondence between the verts and the 3d locations is fixed.
I'm thinking this could be done by solving for the coefficients of a transformation matrix, but I'm a little unsure how to build the system to solve.
An example of this is a cube. The prototype would be the unit cube, centered at the origin, with vert indices:
4----5
|\ \
| 6----7
| | |
0 | 1 |
\| |
2----3
An example of the vert locations to fit:
v0: 1.243,2.163,-3.426
v1: 4.190,-0.408,-0.485
v2: -1.974,-1.525,-3.426
v3: 0.974,-4.096,-0.485
v5: 1.974,1.525,3.426
v7: -1.243,-2.163,3.426
So, given that prototype and those points, how do I find the single scale factor, and the rotation about x, y, and z that will minimize the distance between the verts and those positions? It would be best for the method to be generalizable to an arbitrary mesh, not just a cube.
Assuming you have all points and their correspondences, you can fine-tune your match by solving the least squares problem:
minimize Norm(T*V-M)
where T is the transformation matrix you are looking for, V are the vertices to fit, and M are the vertices of the prototype. Norm refers to the Frobenius norm. M and V are 3xN matrices where each column is a 3-vector of a vertex of the prototype and corresponding vertex in the fitting vertex set. T is a 3x3 transformation matrix. Then the transformation matrix that minimizes the mean squared error is inverse(V*transpose(V))*V*transpose(M). The resulting matrix will in general not be orthogonal (you wanted one which has no shear), so you can solve a matrix Procrustes problem to find the nearest orthogonal matrix with the SVD.
Now, if you don't know which given points will correspond to which prototype points, the problem you want to solve is called surface registration. This is an active field of research. See for example this paper, which also covers rigid registration, which is what you're after.
If you want to create a mesh on an arbitrary 3D geometry, this is not the way it's typically done.
You should look at octree mesh generation techniques. You'll have better success if you work with a true 3D primitive, which means tetrahedra instead of cubes.
If your geometry is a 3D body, all you'll have is a surface description to start with. Determining "optimal" interior points isn't meaningful, because you don't have any. You'll want them to be arranged in such a way that the tetrahedra inside aren't too distorted, but that's the best you'll be able to do.