by julienM, published
This was an exercise in large model simplification. The original has 28 mio. triangles, this simplified version has been reduced to 460 000. Filesize went from 1.4 GB to 21 MB.
Recent Commentsview all
Liked Byview all
Give a Shout Out
Recommended file: Lucy_3M_O10_20%.stl
Update2: A print of the head at 50% scale, printed at a layer height of 0.3, looks very nice. At this scale, the statue would be 70 cm high. I'm now very motivated to build a Rostock derivative, although that would normally only allow 40 cm height.
Update: The mesh was sampled with 3M points, then an out of core Poisson reconstruction applied, followed by quadric edge collapse decimation (to 20%, hence the filename). The result is a 22 MB Mesh much better than the Lucy_simplified, at no extra size. I really need to document this all logically, this might not be the best place for it.
Taken from the Stanford 3D scanning site (graphics.stanford.edu/data/3Dscanrep/). The Stanford CGL asks that this model not be transfomed, morphed etc.
The original file was not manifold, making a straightforward simplification in MeshLab (see www.thingiverse.com/thing:40632) difficult. As a workaround, the statue was subsampled to 500K points in CloudCompare (www.danielgm.net/cc/). A reconstrucion of the resulting point-cloud should be possible with the poisson-reconstruction function in CloudCompare. As I am more familiar in Meshlab, I used that instead.
The workflow for a point-cloud to volume conversion is as follows: Filters > Point Set > Estimate radius from density Filters > Point Set > Compute normals for point sets Filters > Remeshing, etc > Surface Reconstruction: Poisson (I used Octree Depth: 9) Netfabb repair, due to some small errors
Update: I've uploaded the script for a surface reconstruction in MeshLab (.mlx): Filters > Show current filter script Open script > "Surface reconstruction.mlx" <Apply script>
The parameters for all stages in the script can be changed beforehand, then everything can be executed at once. MeshLab also installs a server, so the script can be executed from the command line. This however is more useful when working on a lot of models at once.
Slicing at 25% scale (200 mm wide at 45Ã‚Â°, 400 mm high) for a maximum Rostock build goes to 6 GB in RAM on my machine, so it is probably impractical to use a model with less simplification. The original model, converted to .stl, can surpass 14 GB in RAM when manipulated in Meshlab, so the point-cloud has also been included (.asc), for those wanting to try out this method. The original Model can be downloaded from the Stanford CGL site.