Description: BMyCrust take as input a 3D scatter points cloud and return a tight, manifold, triangulation.
Remember that crust algorithm needs a cloud representing a volume, so open surface may give inaccurate results. For example : a plane can not be triangulated, half-sphere is in doubt, a sphere with a small hole shoud be good.
The more points are given the best the surface will be fitted, of course in this case you would have to wait more and in the worst case a memory help error may occurs. The best results are obtained with more points in high curvature feature.
The old version did not ensure the output surface to be a manifold so it could be used only for graphical purpose. In the new one a tight, regular manifold is returned. It as outward normals orientation, after using this algorithm is very easy to get an STL file from a point cloud.
I added a manifold extraction tool that also correct the errors (slivers) generated by delaunayn during the initial tessellation.-BMyCrust take as input a 3D scatter points cloud and return a tight, manifold, triangulation.
Remember that crust algorithm needs a cloud representing a volume, so open surface may give inaccurate results. For example : a plane can not be triangulated, half-sphere is in doubt, a sphere with a small hole shoud be good.
The more points are given the best the surface will be fitted, of course in this case you would have to wait more and in the worst case a memory help error may occurs. The best results are obtained with more points in high curvature feature.
The old version did not ensure the output surface to be a manifold so it could be used only for graphical purpose. In the new one a tight, regular manifold is returned. It as outward normals orientation, after using this algorithm is very easy to get an STL file from a point cloud.
I added a manifold extraction tool that also correct the errors (slivers) generated by delaunayn during the initial tessellation. Platform: |
Size: 6042624 |
Author:naz |
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Description: Compute nearest neighbours (by Euclidean distance) to a set of points of interest from a set of candidate points.
The points of interest can be specified as either a matrix of points (as columns) or indices into the matrix of candidate points.
Points can be of any (within reason) dimension.
nearestneighbour can be used to search for k nearest neighbours, or neighbours within some distance (or both)
If only 1 neighbour is required for each point of interest, nearestneighbour tests to see whether it would be faster to construct the Delaunay Triangulation (delaunayn) and use dsearchn to lookup the neighbours, and if so, automatically computes the neighbours this way. This means the fastest neighbour lookup method is always used. Platform: |
Size: 30720 |
Author:nadir |
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Search - Delaunayn matlab