I tried to develop a thermal model by using an orphan mesh part(DC3D8R) but when I run the model, the following error was displayed:
'The volume of 13124 elements is zero, small, or negative. Check coordinates or node numbering, or modify the mesh seed. In the case of a tetrahedron this error may indicate that all nodes are located very nearly in a plane. The elements have been identified in element set ErrElemVolSmallNegZero.'
Please, how can I fix this error ?
Thank you !
Related
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.
I want to find multiple objects in a scene (objects look the same, but may differ in scale, and rotation and I don´t know what the object to be detected will be). I have implemented the following idea, based on the featuredetectors in OpenCV, which works:
detect and compute keypoints from the object
for i < max_objects_todetect; i++
1. detect and compute keypoints from the whole scene
2. match scene and object keypoints with Flannmatcher
3. use findHomography/Ransac to compute the boundingbox of the first object (object which hast the most keypoints in the scene with multiple objects)
4. set the pixel in the scene, which are within the computed boundingbox to 0, -> in the next loopcycle there are no keypoints for this object to detect anymore.
The Problem with this implementation is that I need to compute the keypoints for the scene multiple times which needs alot of computing time (250ms). Does anyone has a better idea for detecting multiple objects?
Thanks Drian
Hello togehter I tried ORB which is indeed faster and I will try Akaze.
While testing ORB I have encouterd following problem:
While chaning the size of my picture doesn´t affect the detected keypoints in Surf (finds the same keypoints in the small and in the big picture (in the linked picture right)), it affects the keypoints detected by ORB. In the small picute i´m not able to find these keypoints. I tried to experiment with the ORB parameters but couldn´t make it work.
Picture: http://www.fotos-hochladen.net/view/bildermaf6d3zt.png
SURF:
cv::Ptr<cv::xfeatures2d::SURF> detector = cv::xfeatures2d::SURF::create(100);
ORB:
cv::Ptr<cv::ORB> detector = cv::ORB::create( 1500, 1.05f,16, 31, 0, 2, ORB::HARRIS_SCORE, 2, 10);
Do you know if, and how it´s possible to detect the same keypoints independent of the size of the pictures?
Greetings Drian
i want to get distance from depth image .i have 3D position and i convert this point to 2D.know i read this coordinate data from RGB and depth image .
iin this picture i mark 5 point and see this point data from depth image.
why this coordinate data is not correct ?
or why this data is 0 ?
how can i get real data from depth image ?
The data returned by Kinect has 0 values at pixels it fails to get the depth measurement. This can happen due to several reasons but especially happens at reflective surfaces or glass etc.
Also, the values around the edges are more error prone, so there are more 0 values around the edges than the center of the image.
So, (a) and (d) can be zero, but if you check the nearby pixels there will be depth values. (e) is a bad measurement, as it has a large value, whereas it is closer than rest of the points.
Before acquiring data, maybe you can try some hole filling technique such as Depth Normalization on your data, to remove most of the zero values.
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.
In my meshing application I will have to specify fix points within a domain. The idea is that, the fix points must also be the element points after the domain is being meshed.
Furthermore, the elements around the fix points should be more dense. The general concept is that for the fix points, there should exist a radius r around those points, such that the mesh size inside r is of different sizes than outside of the r. The mesh sizes inside and outside of the r should be specifiable.
Are these two things doable in CGAL 2D Mesh algorithm?
Using your wording, all the input point of the initial constrained Delaunay triangulation will be fix points, because the 2D mesh generator only insert new points in the triangulation: it never removes any point.
As for the density, you can copy, paste, and modify a criteria class, such as CGAL::Delaunay_mesh_size_criteria_2<CDT> so that the local size upper bound is smaller around the fix points.
Now, the difficulty is how to implement that new size policy. Your criteria class could store a const reference to another Delaunay_triangulation_2, that contains only the fixed points you want. Then, for each triangle query, you can call nearest_vertex and then actually check if the distance between the query point is smaller that the radius bound of your circles. For a triangle, you can either verify that for only its barycenter, or for all three points of the triangle. Then, according to the result of that/those query(s), you can modify the size bound, in the code of your copy of CGAL::Delaunay_mesh_size_criteria_2<CDT>.
Yes, no points will be removed from the triangulation by the mesher.
Note however that if you insert points too close to a constraint this will induce a refinement of the constraint while it is not Gabriel.