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A Photogrammetry-based Framework to Facilitate Image-based Modeling and Automatic Camera Tracking

 
: Bullinger, Sebastian; Bodensteiner, Christoph; Arens, Michael

:

Sousa, A. ; Institute for Systems and Technologies of Information, Control and Communication -INSTICC-, Setubal; International Association for Pattern Recognition -IAPR-:
16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Proceedings. Vol.1: GRAPP : February 8-10, 2021
Setúbal: SciTePress, 2021
ISBN: 978-989-758-488-6
S.106-112
International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP) <16, 2021, Online>
International Conference on Computer Graphics Theory and Applications (GRAPP) <16, 2021, Online>
Englisch
Konferenzbeitrag
Fraunhofer IOSB ()
Image-based Modeling; camera tracking; Structure-from-Motion; photogrammetry

Abstract
We propose a framework that extends Blender to exploit Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques for image-based modeling tasks such as sculpting or camera and motion tracking. Applying SfM allows us to determine camera motions without manually defining feature tracks or calibrating the cameras used to capture the image data. With MVS we are able to automatically compute dense scene models, which is not feasible with the built-in tools of Blender. Currently, our framework supports several state-of-the-art SfM and MVS pipelines. The modular system design enables us to integrate further approaches without additional effort. The framework is publicly available as an open source software package.

: http://publica.fraunhofer.de/dokumente/N-624980.html