How to do mesh segmentation in Matlab

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maho
maho 2019-7-4
编辑： TED MOSBY 2024-11-18，19:51
Hello,
my goal is to approximate/represent CAD-Meshes with primitive bodies, eg. cuboids, spheres, cylinders etc. in Matlab. I consider the segmentation of the models into coherent regions an important step towards that goal.
The following figures show a trisurf'd example model, out of which I would like to extract several cylinders and probably some cuboids.
tailstork1.png tailstork2.png
I am aware that there are several scientific papers on mesh segmentation and also some on primitive extraction. However, I have never before programmed an entire algorithm from a scientific paper.
What I am asking is: Does someone know a practical way of doing this in Matlab, especially some source code samples / Toolboxes for mesh segmentation and/or 3d primitive approximation?
Until know, I have found an tried:
  • Learning-Mesh-Segmentation, (I dont have the computing ressources to train the model), from here
  • ddcrpMeshSegmentation, requires multiple moved versions of the same model, from here
  • K-means, modified from this blog
  • some graph partitioning functions, many available, eg. here
Out of those, K-means looks somewhat promising and maybe some graph partitioning might also help. For the following primitive approximation, minBoundEllipsoids seems like a good way to extract orientations from the segments to me. I appreciate comments on that too.
Thank you very much
  1 个评论
Lorenzo Pollicini
Lorenzo Pollicini 2024-10-16
Hello @maho, did you find a solution for your problem? I am also dealing with the same task at the moment.

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回答（1 个）

TED MOSBY
TED MOSBY 2024-11-14，14:35
编辑：TED MOSBY 2024-11-18，19:51
Hi @maho,
To segment a mesh into coherent regions you can try one of these methods:
1. K-means clustering: Use the built-in ‘kmeans’ function.
www.mathworks.com/help/stats/kmeans.html?searchHighlight=kmeans&s_tid=srchtitle_support_results_1_kmeans
2. Graph Partitioning: You can use MATLAB's ‘graph’ or ‘digraph’ functions for this purpose.
www.mathworks.com/help/matlab/ref/graph.html?searchHighlight=graph&s_tid=srchtitle_support_results_1_graph
www.mathworks.com/help/matlab/math/partition-graph-with-laplacian-matrix.html?searchHighlight=graph%20partition&s_tid=srchtitle_support_results_1_graph%2520partition
3. Spectral clustering: it uses the eigenvectors of a graph Laplacian to perform clustering.
www.mathworks.com/help/stats/spectral-clustering.html?searchHighlight=spectral%20clustering&s_tid=srchtitle_support_results_1_spectral%2520clustering[UN1]
Now after you are done with the segmentation, to fit primitive shapes in each segment, you can:
You can follow the above steps like in an example workflow here:
% Load mesh data (vertices and faces)
[vertices, faces] = readObj('your_mesh_file.obj');
% Compute normals or other features
normals = computeNormals(vertices, faces);
% Apply K-means clustering
numClusters = 5;
[idx, C] = kmeans(normals, numClusters);
% Visualize segmentation
trisurf(faces, vertices(:,1), vertices(:,2), vertices(:,3), idx);
% Fit primitives to each segment
for i = 1:numClusters
segmentIndices = (idx == i);
segmentVertices = vertices(segmentIndices, :);
% Fit a bounding box or other primitive
bbox = fitBoundingBox(segmentVertices); %custom function, make on your own
plotBoundingBox(bbox);%custom function, make on your own
end
Hope this helps!

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