Project

CAPTURE

CAPTURE - 3D-scene reconstruction with high resolution and high dynamic range spherical images

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Reconstruction of 3D-scenes out of camera images represents an essential technology for many applications, such as 3D-digital-cities, digital cultural heritages, games, tele-cooperation, tactical training or forensic. The objective of the project CAPTURE is to develop a novel approach for 3D scene acquisition and develop corresponding theory and practical methods.

Instead of processing a large amount of standard perspective low resolution video images, we use as input data a few single but full spherical high resolution and high dynamic range (HDR) images. Currently available spherical high resolution cameras are able to record fine texture details and the complete scene from a single point in space. Additionally such cameras provide HDR images yielding consistent color and photometric information. We propose to exploit this new technology focusing on the dense/high-quality 3D reconstruction of both indoor and outdoor environments.

The fundamental issue of the project is to develop novel algorithms that take into account the properties of these images, and thus to push forward the current state of the art in 3D scene acquisition and viewing. In particular we develop novel stable and light-invariant image feature detectors, as well as robust assignment methods for image matching and novel 3D reconstruction/viewing algorithms, which exploit the properties of the images.

The multiple spherical view geometry provides a high amount of redundant information about the underlying environment. This, combined with the consistency of the color and photometric information from HDR images, allows us to develop new methods for robust high-precision image matching and 3D structure estimation, resulting in a high-fidelity textured model of the real scene.

The development of the project CAPTURE makes extensive usage of our Computer Vision Development Framework ARGOS. From the software development side, it is necessary to work with large images and merge information from multiple sources simultaneously. We therefore also put special attention in parallel processing of large amount of data as well as clustering capabilities.

The application of this project is the accurate reconstruction of large scenes which includes industrial facilities, touristic and cultural heritage sites, as well as urban environments.

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Publications about the project

Bernd Krolla, Christiano Couto Gava, Alain Pagani, Didier Stricker

In: Vaclav Skala (editor). Communication Papers Proceedings of the 22nd International Conference on Computer Graphics, Visualization and Computer Vision |. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG-2014) 22nd June 2-5 Pilzen Czech Republic ISBN 978-80-86943-71-8 University of West-Bohemia 2014.

To the publication
Alain Pagani, Didier Stricker

In: Proceedings of the 11th Workshop on Omnidirectional Vision, Camera Networks and Non-classical Cameras. Workshop on Omnidirectional Vision, Camera Networks and Non-classical Cameras (OMNIVIS-2011) November 6-13 Barcelona Spain IEEE 11/2011.

To the publication
Alain Pagani, Christiano Couto Gava, Yan Cui, Bernd Krolla, Jean-Marc Hengen, Didier Stricker

In: F. Niccolucci, M. Dellepiane (editor). 12th International Symposium on Virtual Reality, Archeology, and Cultural Heritage |. International Symposium on Virtual Reality, Archaeology and Cultural Heritage (VAST-2011) 12th October 19-21 Prato Italy Pages 17-24 ISBN 978-3-905674-34-7 Eurographics Association Goslar, Germany 10/2011.

To the publication

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz