13 ms·
There is a lot of academic work in this area that's relevant, dating back at least 10 years. I'm surprised it wasn't mentioned. Maximizing the entropy of proje
by cscheid 13y ago
There is a lot of academic work in this area that's relevant, dating back at least 10 years. I'm surprised it wasn't mentioned.
Maximizing the entropy of projected normals works well, for example, like this paper by Polonsky et al.: http://www.cs.technion.ac.il/~gotsman/AmendedPubl/Oleg/Polonsky_et_al.pdf http://www.cs.technion.ac.il/~gotsman/AmendedPubl/Oleg/Polon...
A related problem is automatically figuring out the "natural orientation" of a mesh, so your automatic renderings don't look upside down by accident. Fu et al. use pretty simple machine learning tools: http://www.cs.ubc.ca/nest/imager/tr/2008/upright_orientation/ http://www.cs.ubc.ca/nest/imager/tr/2008/upright_orientation...
- simcop2387 13y agoI think in this particular case you should be able to actually use the coordinate system in the STL for that. most things for printing you'll likely want to see how it would be coming out of the printer so you'll have an actual up direction defined before you do this. Should save a lot of computation there.
- luchak 13y agoSecord et al. also did a perceptual study in which they determined which features were most correlated with user viewpoint preference and built models for viewpoint preference based on this information: http://gfx.cs.princeton.edu/pubs/Secord_2011_PMO/index.php http://gfx.cs.princeton.edu/pubs/Secord_2011_PMO/index.php
- jjs 13y agoInteresting. The OP got me thinking along the lines of manually tagging salient features of each model (as well as ranking models by salience, either manually or automatically based on criteria related to the object that the model represents).