What to achieve?

Project Goals

In the past decade, significant effort has been put in the direction of developing robust methodologies and supporting technology for enabling and populating specialized repositories of digitized cultural heritage (CH) artifacts. Many EU and worldwide initiatives and funded projects have contributed to defining best practices for digitization of CH objects. Furthermore, a systematic effort has been focused on the process of cataloguing and enrichment of the available digital CH object versions with ontological metadata, capable of conveying meaningful interpretations in a multitude of application scenarios such as on-line digital repositories and virtual reconstructions. Up to now, digitized data have been exploited in their existing form in numerous CH application scenarios. The rapidly increasing digitized CH content however, can be used to provide feedback to the generation of new information itself. PRESIOUS will research and develop innovative methods and technologies to augment the geometric data of digital CH objects by predictive generative processes guided by geometry processing, analysis and shape matching methods for 3D objects resulting in:

• Innovative and competitive European products for the CH market, given that Europe has the largest density of CH institutions (users) among all continents.
• A reduction of the effort and cost involved in 3D digitization by an order of magnitude and improved data quality through radical re-design of the digitization process.
• An innovative approach to computer-assisted CH object restoration and preservation that boosts efficiency and reduces time and cost.

Technology with potential exploitation transcending the CH domain.

Scientific Objectives

Based on the acknowledged expertise of the technical partners of the consortium, a core set of techniques for 3D matching, retrieval and metrology have been transformed into specialized predictive reconstruction technologies targeting the following objectives:

• On-the-fly auto-completion for 3D digitization. The shape of digitized CH objects can be potentially predicted during acquisition, based on the gradually available partial scans of an object. The stream of input point cloud data from the acquisition source is used to interactively retrieve and fit the closest matching candidate shape from parts of digitized artifact repository models as well as template models (categorized primitive objects), onto the acquired geometry, thus predicting and automatically suggesting the geometry for the parts not yet scanned. As the acquisition data are the starting point for all subsequent processing steps, their quality and reliability are significant issues. Corrections and fine details can be locally applied, where necessary, using localized complementary scans, effectively minimizing the overall time and cost of the scanning procedure or eliminating the need to post-process the data or attempt scanning in hard to reach surfaces of the original CH artifact. 

  CH objects are especially good candidates for such a system since they can often be categorized, possess regularity, symmetries or repeated patterns and salient features. Furthermore, typical acquisition cases involve immovable, large or heavy to lift parts and fragments, which can be digitized in place, since inaccessible parts could be predicted though auto-completion. 

• Estimation and prediction of monument degradation. Based on present-time surface shape, material measurements and environmental data, the project investigated highly-efficient techniques for forward and inverse deterioration prediction. This allows to essentially move the artifact's surface condition "forward in time" and visualize the dynamic state of the deteriorating object, in the context of geometric and textural alterations. In order to include  geometric information in the simulation model, PRESIOUS conducted a number of timed, high-accuracy differential surface scans on the degrading monuments. Also, using the digitized data of the monument in its current state, similar surface regions have been retrieved and fitted to the degrading surface. This acts as an additional constraint for the simulation by providing an indication of the intact state of the object.

• 3D CH fractured object restoration and completion (missing parts synthesis). By exploiting existing CH objects in an example-based object restoration process, automated procedures have been developed for fractured artifact reassembly in three dimensions. This resembles the solution to a three-dimensional puzzle, where the pieces are either intact or broken artifacts retrieved from relevant CH object repositories and the target result is predicted from approximate model templates, which act as constraining guides. The developed techniques rely on shape analysis and processing techniques, similar in nature to the predictive scanning problem. Subsequently, novel techniques for the recovery (prediction) and automatic geometry generation of missing elements were developed; the missing elements prediction is conducted at multiple levels of detail (general shape, detail sculpting) and can thereby aid the physical repair process of the actual objects.

Facts

PRESIOUS is a collaborative 3-year STREP project, which started on 1 Feb 2013, with 5 participants spread across three countries: Norway, Germany and Greece. The total budget of the project amounts to 3,597,408€, with an EU contribution of 2,728,430€. PRESIOUS was funded under the ICT-2011.8.2 theme of the 7th Framework Programme (grant agreement number: 600533). 

The work in PRESIOUS is divided into 6 work packages: 1 management WP (type MGT), 4 RTD WPs and 1 dissemination WP (type OTHER). Three out of the four RTD work packages are core research activities corresponding to the three scientific objectives of the project. The forth RTD work package is dedicated to the integration and evaluation of two platforms, one for the reassembly and stone degradation prediction and one for the predictive scanning.