Modern photogrammetric methods open up the possibility of virtually reconstructing large entities such as buildings as well as small-scale objects like keys in highly detailed 3D models with minimal time and effort. For this purpose, the researchers of our Multiple Senses Group have developed a photogrammetric key scanner together with our corporate partner Entrich Technologies. In this so-called "Lightbox," stereoscopic 3D reconstructions of keys of any kind are produced by a specially designed illumination setup with rotating camera positions.
Moreover, thanks to specially adapted deep learning methods, the Lightbox becomes "smart": the key's brand and type can also be determined automatically during the scanning process.
The Lightbox 2.0 prototype is currently being tested and evaluated in real production environments by our corporate partner Entrich Technologies. The patent application is in preparation.
Start: 04.2019
End Date: 12.2020
Entrich Technologies
FFG - COMET, Project Surfaces
The research goal of AMASE is to create a suite of tools and methods to ingest, process, visualize, and manipulate heterogeneous, large-scale geospatial data. This data is the constantly updated representation of the real world in the form of an evolving digital twin.
The main goal of this project is to enable a reliable decision support for large-scale infrastructure projects by providing solutions for a collaborative visual analysis of digital twins.
The main objective of the strategic project ARCS is the design of software architectures that enable interactive visualization systems to ingest large volumes and velocities of geospatial and associated non-geometric data.
Visualization and visual analysis of high-resolution surface reconstructions find a wide range of applications, from tunnel monitoring and archaeological excavations to the change management of cultural heritage buildings.
The "Mars-DL" project is investigating how a deep learning system can support the research work of planetary scientists through object and pattern recognition. For this project VRVis has extended the functionality of PRo3D to automatically render shatter cone training images.
MINERVA is an integrated framework for planetary scientists allowing members of different instrument teams to cooperate synergistically in virtual workspaces by sharing observations, analyses and annotations of heterogonous mission data.
In this project tools and methods for handling, administration, manipulation and evaluation of several different data sources for measurements and lighting design are developed.
Support for planetary research: Visual analysis of reconstructions of the Mars surface and view planning for rover camera instruments.
Virtual exploration and geological analysis of reconstructed Mars surfaces and rock outcrops.
In order to preserve the architectural heritage, we use methods of photogrammetry, thermography, photometry as well as laser scans to carry out inventories, recognition and documentation of changes in protected buildings.
