How to analyse 3d mesh data(in .stl) by TensorFlow
You have to first extract "features" out of your dataset. These are fixed-dimension vectors. Then you have to define labels which define the prediction. Then, you have to define a loss function and a neural network. Put that all together and you can train a classifier.
In your example, you would first need to extract a fixed dimension vector out of an object. For instance, you could extract the object and project it on a fixed support on the x, y, and z dimensions. That defines the features.
For each object, you'll need to label whether it's convex or concave. You can do that by hand, analytically, or by creating objects analytically that are known to be concave or convex. Now you have a dataset with a lot of sample pairs (object, is-concave).
For the loss function, you can simply use the negative log-probability.
Finally, a feed-forward network with some convoluational layers at the bottom is probably a good idea.
I try to write an script in python for analyse an .stl data file(3d geometry) and say which model is convex or concave and watertight and tell other properties...
I would like to use and TensorFlow, scikit-learn or other machine learning library. Create some database with examples of objects with tags and in future add some more examples and just re-train model for better results.
But my problem is: I don´t know how to recalculate or restructure 3d data for working in ML libraries. I have no idea.
Thank you for your help.