Self-learning Voxel-based Multi-camera Occlusion Maps for 3D Reconstruction

Maarten Slembrouck, Dimitri Van Cauwelaert, David Van Hamme, Dirk Van Haerenborgh, Peter Van Hese, Peter Veelaert, Wilfried Philips

2014

Abstract

The quality of a shape-from-silhouettes 3D reconstruction technique strongly depends on the completeness of the silhouettes from each of the cameras. Static occlusion, due to e.g. furniture, makes reconstruction difficult, as we assume no prior knowledge concerning shape and size of occluding objects in the scene. In this paper we present a self-learning algorithm that is able to build an occlusion map for each camera from a voxel perspective. This information is then used to determine which cameras need to be evaluated when reconstructing the 3D model at every voxel in the scene. We show promising results in a multi-camera setup with seven cameras where the object is significantly better reconstructed compared to the state of the art methods, despite the occluding object in the center of the room.

Paper Citation

in Harvard Style

Slembrouck M., Van Cauwelaert D., Van Hamme D., Van Haerenborgh D., Van Hese P., Veelaert P. and Philips W. (2014). Self-learning Voxel-based Multi-camera Occlusion Maps for 3D Reconstruction . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 502-509. DOI: 10.5220/0004723305020509

in Bibtex Style

@conference{visapp14,
author={Maarten Slembrouck and Dimitri Van Cauwelaert and David Van Hamme and Dirk Van Haerenborgh and Peter Van Hese and Peter Veelaert and Wilfried Philips},
title={Self-learning Voxel-based Multi-camera Occlusion Maps for 3D Reconstruction},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)},
year={2014},
pages={502-509},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004723305020509},
isbn={978-989-758-004-8},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: VISAPP, (VISIGRAPP 2014)
TI - Self-learning Voxel-based Multi-camera Occlusion Maps for 3D Reconstruction
SN - 978-989-758-004-8
AU - Slembrouck M.
AU - Van Cauwelaert D.
AU - Van Hamme D.
AU - Van Haerenborgh D.
AU - Van Hese P.
AU - Veelaert P.
AU - Philips W.
PY - 2014
SP - 502
EP - 509
DO - 10.5220/0004723305020509