CAPTURE - 3D-scene reconstruction with high resolution and high dynamic range spherical images
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.
- Alain Pagani; Johannes Köhler; Didier Stricker
Circular markers for camera pose estimation.
In: Proceedings of the 12th International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS 2011). International Workshop on Image Analysis for Multimedia Interactive Services (WIAMIS-2011), 12th, April 13-15, Delft, Netherlands, IEEExplore, 2011.
- Yan Cui; Alain Pagani; Didier Stricker
Robust point matching in HDRI through estimation of illumination distribution.
In: Proceedings of the 33rd Annual Pattern Recognition Symposium of the German Association for Pattern Recognition (DAGM) 2011. Annual Symposium of the German Association for Pattern Recognition (DAGM-2011), 33rd, August 30 - September 2, Frankfurt am Main, Germany, o.A. 9/2011.
- Alain Pagani; Christiano Gava; Yan Cui; Bernd Krolla; Jean-Marc Hengen; Didier Stricker
Dense 3D Point Cloud Generation from Multiple High-Resolution Spherical Images.
In: F. Niccolucci; M. Dellepiane (Hrsg.). 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.
- Alain Pagani; Didier Stricker
Structure from Motion using full spherical panoramic cameras.
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.