Skip to main content

Real-Time GPU-Based Voxel Carving with Systematic Occlusion Handling

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5748))

Abstract

We present an approach to compute the visual hulls of multiple people in real-time in the presence of occlusions. We prove that the resulting visual hulls are correct and minimal under occlusions. Our proposed algorithm runs completely on the GPU with framerates up to 50fps for multiple people using only one computer equipped with off-the-shelf hardware. We also compare runtimes for different graphic chips and show that our approach scales very well without additional effort. Comparison to other work shows that our algorithm is as fast as state-of-the-art technology. The resulting visual hulls can be the basis for a wide range of algorithms that require a robust voxel representation as input.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Laurentini, A.: The visual hull concept for silhouette-based image understanding. IEEE Transactions on Pattern Analysis and Machine Intelligence 16, 150–162 (1994)

    Article  Google Scholar 

  2. Li, M., Magnor, M., Seidel, H.-P.: Hardware-accelerated visual hull reconstruction and rendering. Graphics Interface, 65–71 (2003)

    Google Scholar 

  3. Guan, L., Sinha, S., Franco, J.-S., Pollefeys, M.: Visual Hull Construction in the Presence of Partial Occlusion. In: Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission, pp. 413–420 (2006)

    Google Scholar 

  4. Guan, L., Franco, J.-S., Pollefeys, M.: 3D Occlusion Inference from Silhouette Cues. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)

    Google Scholar 

  5. Kim, H., Sakamoto, R., Kitahara, I., Orman, N., Toriyama, T., Kogure, K.: Compensated Visual Hull for Defective Segmentation and Occlusion. In: 17th International Conference on Artificial Reality and Telexistence, pp. 210–217 (2007)

    Google Scholar 

  6. Ladikos, A., Benhimane, S., Navab, N.: Efficient visual hull computation for real-time 3D reconstruction using CUDA. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–8 (2008)

    Google Scholar 

  7. Luck, J., Small, D., Little, C.Q.: Real-Time Tracking of Articulated Human Models Using a 3D Shape-from-Silhouette Method. In: Proceedings of the International Workshop on Robot Vision, pp. 19–26 (2001)

    Google Scholar 

  8. Kehl, R., Bray, M., Van Gool, L.: Full body tracking from multiple views using stochastic sampling. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 129–136 (2005)

    Google Scholar 

  9. Caillette, L., Howard, T.: Real-Time Markerless Human Body Tracking with Multi-View 3-D Voxel Reconstruction. In: Proc. ISMAR, pp. 597–606 (2004)

    Google Scholar 

  10. Erol, A., Bebis, G., Boyle, R.D., Nicolescu, M.: Visual Hull Construction Using Adaptive Sampling. In: Proceedings of the Seventh IEEE Workshops on Application of Computer Vision, vol. 1, pp. 234–241 (2005)

    Google Scholar 

  11. NVIDIA Cuda (March 2009), http://www.nvidia.com/cuda

  12. Lindholm, E., Nickolls, J., Oberman, S., Montrym, J.: NVIDIA Tesla: A Unified Graphics and Computing Architecture. IEEE Micro 28, 39–55 (2008)

    Article  Google Scholar 

  13. Garland, M., Le Grand, S., Nickolls, J., Anderson, J., Hardwick, J., Morton, S., Phillips, E., Zhang, Y., Volkov, V.: Parallel Computing Experiences with CUDA. IEEE Micro 28, 13–27 (2008)

    Article  Google Scholar 

  14. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 511–518 (2001)

    Google Scholar 

  15. Fung, J., Mann, S.: Using graphics devices in reverse: GPU-based Image Processing and Computer Vision. In: IEEE International Conference on Multimedia and Expo, pp. 9–12 (2008)

    Google Scholar 

  16. Seitz, S.M., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A Comparison and Evaluation of Multi-View Stereo Reconstruction Algorithms. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 519–528 (2006)

    Google Scholar 

  17. Middlebury data sets (March 2009), http://vision.middlebury.edu/mview/data/

  18. AXIS Communications (March 2009), http://www.axis.com/

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Schick, A., Stiefelhagen, R. (2009). Real-Time GPU-Based Voxel Carving with Systematic Occlusion Handling. In: Denzler, J., Notni, G., Süße, H. (eds) Pattern Recognition. DAGM 2009. Lecture Notes in Computer Science, vol 5748. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03798-6_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-03798-6_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-03797-9

  • Online ISBN: 978-3-642-03798-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics