Machine-learning for 3D modeled object inference

A computer-implemented method of applying a neural network learnable according to a method of machine-learning, the method of machine-learning comprising:

• obtaining a dataset including 3D modeled objects which each represent a respective mechanical part, the dataset having one or more sub-datasets, each sub-dataset comprising 3D modeled objects of the dataset and forming at least a part of the dataset;
• and
• for each respective sub-dataset:
• determining a base template, the base template being a 3D modeled object which represents a centroid of the 3D modeled objects of the sub-dataset and which represents a 3D mean geometrical shape of the 3D shapes of the 3D modeled objects of the sub-dataset according to a distance between 3D shapes, and
• learning a neural network configured for inference of deformations of the base template each into a respective 3D modeled object, the learning including a training based on the sub-dataset.

A device comprising:

• a processor;
• and
• a non-transitory data storage medium having recorded thereon a computer program comprising instructions for machine-learning that when executed by the processor causes the processor to
• obtain a dataset including 3D modeled objects which each represent a respective mechanical part, the dataset having one or more sub-datasets, each sub-dataset comprising 3D modeled objects of the dataset and forming at least a part of the dataset, and
• for each respective sub-dataset:
• determine a base template, the base template being a 3D modeled object which represents a centroid of the 3D modeled objects of the sub-dataset and which represents a 3D mean geometrical shape of the 3D shapes of the 3D modeled objects of the sub-dataset according to a distance between 3D shapes, and
• learn a neural network configured for inference of deformations of the base template each into a respective 3D modeled object, the learning including a training based on the sub-dataset.