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.

References

  1. Apostoloff, N. and Fitzgibbon, A. (2005). Learning spatiotemporal t-junctions for occlusion detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 2, pages 553-559. IEEE.
  2. Brostow, G. J. and Essa, I. A. (1999). Motion based decompositing of video. In Computer Vision, 1999. The Proceedings of the Seventh IEEE International Conference on, volume 1, pages 8-13. IEEE.
  3. Corazza, S., Mündermann, L., Chaudhari, A., Demattio, T., Cobelli, C., and Andriacchi, T. (2006). A markerless motion capture system to study musculoskeletal biomechanics: Visual hull and simulated annealing approach. Annals of Biomedical Engineering, 34(6):1019-1029.
  4. Corazza, S., Mündermann, L., Gambaretto, E., Ferrigno, G., and Andriacchi, T. P. (2010). Markerless motion capture through visual hull, articulated icp and subject specific model generation. International journal of computer vision, 87(1-2):156-169.
  5. Favaro, P., Duci, A., Ma, Y., and Soatto, S. (2003). On exploiting occlusions in multiple-view geometry. In Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, pages 479-486. IEEE.
  6. Grauman, K., Shakhnarovich, G., and Darrell, T. (2003). A bayesian approach to image-based visual hull reconstruction. In Computer Vision and Pattern Recognition, 2003. Proceedings. 2003 IEEE Computer Society Conference on, volume 1, pages I-187. IEEE.
  7. Guan, L., Sinha, S., Franco, J.-S., and Pollefeys, M. (2006). Visual hull construction in the presence of partial occlusion. In 3D Data Processing, Visualization, and Transmission, Third International Symposium on, pages 413-420. IEEE.
  8. Kim, H., Sakamoto, R., Kitahara, I., Toriyama, T., and Kogure, K. (2006). Robust foreground segmentation from color video sequences using background subtraction with multiple thresholds. Proc. KJPR, pages 188-193.
  9. Laurentini, A. (1994). The visual hull concept for silhouette-based image understanding. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 16(2):150-162.
  10. Zivkovic, Z. (2004). Improved adaptive gaussian mixture model for background subtraction. In Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on, volume 2, pages 28-31 Vol.2.
Download


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