Scene2Model is a modelling environment combining physical and digital modelling. This is achieved by offering an ADOxx-based modelling tool, domain-specific Scene2Model icon libraries and various usage settings to enable the recognition of physical objects (e.g., paper figures) and automatically transform them into a digital representation within a conceptual model.
Getting Started with Scene2Model
The following sections will allow you to get started with Scene2Model!
Introduction and Background
Developed initially in the context of the DIGITRANS project by the OMiLAB@University of Vienna node, the Scene2Model tool supports haptic modelling through paper figures and an automated transformation to digital conceptual models by applying image/QR recognition and semantic technologies. Since then in it was further developed within OMiLAB in the DigiFoF and FAIRWork EU-funded projects.
Further details on the Scene2Model project and its results are available here.
Download and install on your local PC/Mac the latest release of the Scene2Model Modelling Toolkit. We provide the installation package for Microsoft Windows and MacOS (experimental release, utilizing Wine-based virtualisation).
Packages for other operating systems are currently not provided.
Configure Scene2Model with your own, domain-specific semantics and iconic representations. A Scene2Model Icon library contains the digital artefacts (images, photos) to be used during the digitalization.
To get you started, download the skeleton of the icon library and follow the steps outlined in the tutorial video. You have a great library ready? Share with us to extend the S2M library collection. Get in touch with us at s2m@omilab.org.
Depending on your setup, Scene2Model supports different settings to recognize the haptic elements as modelling objects. Select the adequate setting and install it in your environment.
- Hardware Requirements: to run this setting a PC/Mac with administrative access, a Logitech C920 webcam and mounting device is required.
- Software Requirement: The settings package (below) is self-contained. The prerequisite to run it is a functional Docker Desktop installation.
- QR Tags: Prepared tags can be printed here. Additionally, a PDF with the QR Tags is provided with this package. (The QR tags are generated with the ARUCO and there are 250 tags available.)
- Local deployment: The software required for a deployment on a local PC/Mac can be downloaded from here. The package is self-contained, consisting of the image provider, the QR detection service and OLIVE as the interface provider to Scene2Model.