IEIE Transactions on Smart Processing and Computing

The Institute of Electronics and Information Engineers (대한전자공학회)
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Bounding volume estimation algorithm for image-based 3D object reconstruction

  • Received : 2013.11.20
  • Accepted : 2014.02.12
  • Published : 2014.04.30
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Abstract

This paper presents a method for estimating the bounding volume for image-based 3D object reconstruction. The bounding volume of an object is a three-dimensional space where the object is expected to exist, and the size of the bounding volume strongly affects the resolution of the reconstructed geometry. Therefore, the size of a bounding volume should be as small as possible while it encloses an actual object. To this end, the proposed method uses a set of silhouettes of an object and generates a point cloud using a point filter. A bounding volume is then determined as the minimum sphere that encloses the point cloud. The experimental results show that the proposed method generates a bounding volume that encloses an actual object as small as possible.

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References

  1. Laurentini, Aldo. "The visual hull concept for silhouette-based image understanding." IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.16, no.2, pp.150-162, February, 1994. https://doi.org/10.1109/34.273735
  2. Kutulakos et al., "A theory of shape by space carving." International Journal of Computer Vision, vol.38, no.3, pp.199-218, 2000. https://doi.org/10.1023/A:1008191222954
  3. Lee, Jin-Sung, et al. "Efficient three-dimensional object representation and reconstruction using depth and texture maps." Optical Engineering, vol.47, no.1, pp.017204-1-017204-8, January, 2008. https://doi.org/10.1117/1.2832372
  4. Kim, Sujung, et al. "Fast computation of a visual hull." in Proc. ofComputer Vision-ACCV 2010. Springer Berlin Heidelberg, pp.1-10, 2011.
  5. Matusik, Wojciech, et al. "Image-based visual hulls." in Proc. of the 27th annual conference on Computer graphics and interactive techniques. ACM Press/Addison-Wesley Publishing Co., pp.369-374, 2000.
  6. Matsuyama, Takashi, et al. "Real-time threedimensional shape reconstruction, dynamic threedimensional mesh deformation, and high fidelity visualization for three-dimensional video." Computer Vision and Image Understanding, vol.96, no.3, pp.393-434, 2004. https://doi.org/10.1016/j.cviu.2004.03.012
  7. Grau, Oliver et al. "A combined studio production system for 3-D capturing of live action and immersive actor feedback." IEEE Transactions on Circuits and Systems for Video Technology, vol.14, no.3, pp.370-380, 2004. https://doi.org/10.1109/TCSVT.2004.823397
  8. Starck, Jonathan et al. "Surface capture for performance-based animation." IEEEComputer Graphics and Applications, vol.27, no.3, pp.21-31, 2007.
  9. Theobalt, Christian, et al. "High-quality reconstruction from multiview video streams." IEEE Signal Processing Magazine, vol.24, no.6 pp.45-57, 2007. https://doi.org/10.1109/MSP.2007.4317463
  10. Megiddo, Nimrod. "Linear-time algorithms for linear programming in R3 and related problems." IEEEFoundations of Computer Science, 1982. SFCS'08. 23rd Annual Symposium on, 1982.
  11. Skyum, Sven. "A simple algorithm for computing the smallest enclosing circle."Information Processing Letters, vol.37, no.3, pp.121-125, 1991 https://doi.org/10.1016/0020-0190(91)90030-L
  12. Welzl, Emo. "Smallest enclosing disks (balls and ellipsoids)"Springer Berlin Heidelberg, 1991.
  13. Hwang, S.S. et al."High-resolution 3D object Reconstruction using Multiple Cameras."Journal of The Institute of Electronics Engineers of Korea, vol. 50, no.10 pp.2602-2613, Oct. 2013. https://doi.org/10.5573/ieek.2013.50.10.150