On Multi-view Texture Mapping of Indoor Environments using Kinect Depth Sensors

Luat Do, Lingni Ma, Egor Bondarev, Peter H. N. de With

2014

Abstract

At present, research on reconstruction and coloring of 3D models is growing rapidly due to increasing availability of low-cost 3D sensing systems. In this paper, we explore coloring of triangular mesh models with multiple color images by employing a multi-view texture mapping approach. The fusion of depth and color vision data is complicated by 3D modeling and multi-viewpoint registration inaccuracies. In addition, the large amount of camera viewpoints in our scenes requires techniques that process the depth and color vision data efficiently. Considering these difficulties, our primary objective is to generate high-quality textels that can also be rendered on a standard hardware setup using texture mapping. For this work, we have made three contributions. Our first contribution involves the application of a visibility map to efficiently identify visible faces. The second contribution is a technique to reduce ghosting artifacts based on a confidence map. The third contribution yields high-detail textels by adding the mean color and color histogram information to the sigma-outlier detector. The experimental results show that our multi-view texture mapping approach efficiently generates high-quality textels for colored 3D models, while being robust to registration errors.

References

  1. Alshawabkeh, Y. and Haala, N. (2005). Automatic multiimage photo-texturing of complex 3D scenes. XVIII CIPA Int. Symposium, Torino, 27 Sept, pages 1-6.
  2. Bhattacharyya, A. (1943). On a measure of divergence between two statistical populations defined by their probability distributions. Bulletin of Cal. Math. Soc., 35(1):99-109.
  3. Bondarev, E. and Heredia, F. (2013). On photo-realistic 3D reconstruction of large-scale and arbitrary-shaped environments. pages 621-624.
  4. Bradley, C. (2007). The Algebra of Geometry: Cartesian, Areal and Projective Co-Ordinates. Highperception Limited.
  5. Catmull, E. (1974). A subdivision algorithm for computer display of curved surfaces.
  6. Goldluecke, B. and Cremers, D. (2009). Superresolution texture maps for multiview reconstruction. IEEE International Conference on Computer Vision (ICCV).
  7. Grammatikopoulos, L., Kalisperakis, I., Karras, G., and Petsa, E. (2007). Automatic multi-view texture mapping of 3d surface projections. 3D Virtual Reconstruction & Visualization of Complex Architectures, pages 12-13.
  8. Heckbert, P. (1986). Survey of texture mapping. In IEEE Computer Graphics and Applications, pages 56-67.
  9. Hodge, V. and Austin, J. (2004). A Survey of Outlier Detection Methodologies. Artificial Intelligence Review, 22(2):85-126.
  10. Imamura, K., Kuroda, H., and Fujimura, M. (2011). Image content detection method using correlation coefficient between pixel value histograms. In Signal Processing, Image Processing and Pattern Recognition, volume 260, pages 1-9. Springer Berlin Heidelberg.
  11. Pele, O. and Werman, M. (2010). The quadratic-chi histogram distance family. ECCV, (1):1-14.
  12. Rocchini, C., Cignoni, P., and Montani, C. (1999). Multiple Textures Stitching and Blending on 3D Objects. Eurographics Rendering Workshop.
  13. Rocchini, C., Cignoni, P., Montani, C., and Scopigno, R. (2002). Acquiring, stitching and blending diffuse appearance attributes on 3D models. The Visual Computer, 18(3):1-24.
  14. Sainz, M. and Pajarola, R. (2004). Point-based rendering techniques. Computers & Graphics, 28(6):869-879.
  15. Wang, L. and Kang, S. (2001). Optimal texture map reconstruction from multiple views. Computer Vision and pattern recognition (CVPR), pages 1-8.
  16. Whelan, T., Kaess, M., and Fallon, M. (2012). Kintinuous: Spatially Extended KinectFusion.
  17. Whitted, T. (2005). An improved illumination model for shaded display. Communications of the ACM, 23(6):343-349.
  18. Yuksel, C., Keyser, J., and House, D. H. (2010). Mesh colors. ACM Transactions on Graphics, 29(2):1-11.
  19. Zwicker, M., Pfister, H., van Baar, J., and Gross, M. (2001). Surface splatting. Proceedings of the 28th annual conference on Computer graphics and interactive techniques - SIGGRAPH 7801, pages 371-378.
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Paper Citation


in Harvard Style

Do L., Ma L., Bondarev E. and de With P. (2014). On Multi-view Texture Mapping of Indoor Environments using Kinect Depth Sensors . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: PANORAMA, (VISIGRAPP 2014) ISBN 978-989-758-004-8, pages 739-745. DOI: 10.5220/0004875107390745


in Bibtex Style

@conference{panorama14,
author={Luat Do and Lingni Ma and Egor Bondarev and Peter H. N. de With},
title={On Multi-view Texture Mapping of Indoor Environments using Kinect Depth Sensors},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 2: PANORAMA, (VISIGRAPP 2014)},
year={2014},
pages={739-745},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004875107390745},
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: PANORAMA, (VISIGRAPP 2014)
TI - On Multi-view Texture Mapping of Indoor Environments using Kinect Depth Sensors
SN - 978-989-758-004-8
AU - Do L.
AU - Ma L.
AU - Bondarev E.
AU - de With P.
PY - 2014
SP - 739
EP - 745
DO - 10.5220/0004875107390745