Automatic generation of multimodal data of mechanical parts assemblies for machine learning in product reverse engineering – GENERAT3D

Collecting and labelling data for machine learning is a time-consuming task, especially in a multimodal context where it is necessary to have labelling in several types of data (B-Rep models, images, point clouds, etc.). The objective of this project is to set up methods for automatically generating large volumes of labelled data to feed learning methods for the reverse engineering of mechanical parts and assemblies (a problem requiring this multimodality). The originality of the project is based on the fact that it is possible to automatically label certain types of data (e. g. B-Rep surfaces) and to propagate these labels when the other modalities are generated. Methods for generating photorealistic images, synthetic point clouds and depth maps will be developed. Finally, two case studies using these data will demonstrate their usefulness.