CE10 - Industrie et usine du futur : Homme, organisation, technologies

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

Submission summary

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.

Project coordination

ARNAUD POLETTE (Ecole Nationale Supérieure d'Arts et Métiers - Laboratoire d Ingénierie des Systèmes Physiques et Numériques)

The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.

Partner

ENSAM - LISPEN Ecole Nationale Supérieure d'Arts et Métiers - Laboratoire d Ingénierie des Systèmes Physiques et Numériques

Help of the ANR 172,704 euros
Beginning and duration of the scientific project: December 2021 - 42 Months

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