DS0701 -

Semantic reverse-engineering of digital heritage objects – RESEED

Submission summary

This project fits in the field of reverse engineering and meets the requirements and specifications of cultural heritage. The scientific goals are in line with the 7th "ANR Defi", aiming to build our future society based on information and communication.
Nowadays, reverse engineering is widely used in manufacturing industries in order to record geometrical data from the products. However, digitizing physical data does not provide any knowledge about the products. Capitalizing this know-how and knowledge is critical to advance tomorrow's products.
So we have identified two approaches:
- Classical knowledge management based on semantic sources
- Computer assisted design and scanning tools

Yet, there still does not exist any global process: technical issues born from interdisciplinary processes drive us to scientific issues: there is an important need to bring the different models into line and to carry out a case-by-case review of the usual industrial processes.

The ReSeed project aims for the development of a new technology: a tool and an interoperable format in order to digitize both semantic and physical data about objects. Before applying the process on industrial production cases, we decided to experiment our approach on existing assets, related to identified knowledge and whose intrinsic value is firmly established: ReSeed will be tested on the industrial cultural heritage.

In the end, in case of success of the ANR ReSeed program, a cycle between the past and the present knowledge will be established. Actually, protecting, analysing and understanding these objects from the "past heritage" may turn them into "present capital". The transposition of past knowledge into contemporary form, readable and intelligible in our present socio-technical system, may become a source of inspiration to anticipate our future.

Special care will be taken for the quality assessment. Also called performance indicators in the manufacturing industry, ReSeed will implement an ethical code assigned to guarantee the authenticity and uniqueness of the future semantically augmented numerical objects.

As the ANR project is interdisciplinary by nature, the composed consortium consists of:
- 4 research laboratories - 3 of science, 1 of humanities
- 2 companies - 1 of engineering, 1 of humanities
- 2 public bodies - providing use cases at a national scale, in scientific and technical fields.
The expected duration is 48 months; this considers the specific constraints, rules and habits of the involved disciplines.

The potential influences of this project are numerous and spread over several layers: the required employment to deploy the project (2 Phd Students, several professors and engineers are implied) and the economical benefits for the companies providing software and service solutions. Moreover, given the defined ReSeed research fields, the consortium strives have the ambition to provide a new tool for the cultural heritage experts (enabling the semantic digitization of objects). This tool offers multi-level access, so that any citizen could have access to the capitalized knowledge. Among others, the creation of a "3D digitized object" label will be investigated, in agreement with the Huma-Num TGIR (Large Scale Research Infrastructure).

Project coordination

Florent Laroche (Institut de Recherche en Communication et Cybernétique de Nantes)

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.


MAM Musée des Arts-et-Métiers
DeltaCAD DeltaCAD
MCC-MIGPC Ministère de la culture - Mission de l'Inventaire général du patrimoine culturel
UTT Université de Technologie de Troyes
UTC - Roberval Université de Technologie de Compiègne
CFV Université de Nantes - Centre François Viète
IRCCyN Institut de Recherche en Communication et Cybernétique de Nantes

Help of the ANR 692,714 euros
Beginning and duration of the scientific project: November 2016 - 42 Months

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