Advertisement

An Active Patch Model for Real World Appearance Reconstruction

  • Farhad BazyariEmail author
  • Yorgos Tzimiropoulos
Conference paper
  • 3.8k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8925)

Abstract

Dense mapping has been a very active field of research in recent years, promising various new application in computer vision, computer graphics, robotics, etc. Most of the work done on dense mapping use low-level features, such as occupancy grid, with some very recent work using high-level features, such as objects. In our work we use an active patch model to learn the prominent, primitive shapes commonly found in indoor environments. This model is then fitted to coming data to reconstruct the 3D scene. We use Gauss-Newton method to jointly optimize for appearance reconstruction error and geometric transformation differences. Finally we compare our results with Kinect Fusion [6].

Keywords

Dense mapping Deformable patches Gauss-Newton optimization 

References

  1. 1.
    Curless, B., Levoy., M.: A volumetric method for building complex models from range images. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques (ACM) (1996)Google Scholar
  2. 2.
    Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O.: Monoslam: Real-time single camera slam. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(6), 1052–1067Google Scholar
  3. 3.
    Hejrati, M., Ramanan, D.: Analysis by synthesis: 3d object reconstruction by object reconstruction. In: Computer Vision and Pattern Recognition (CVPR) (2014)Google Scholar
  4. 4.
    Klein, G., Murray, D.: Parallel tracking and mapping for small ar workspaces. In: 6th IEEE and ACM International Symposium on Mixed and Augmented Reality, ISMAR (2007)Google Scholar
  5. 5.
    Montemerlo, M., Thrun, S., Koller, D., Wegbreit, B.: FastSLAM: A factored solution to the simultaneous localization and mapping problem. In: Proceedings of the AAAI National Conference on Artificial Intelligence, AAAI, Edmonton, Canada (2002)Google Scholar
  6. 6.
    Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohi, P., Shotton, J., Hodges, S., Fitzgibbon, A.: Kinectfusion: real-time dense surface mapping and tracking. In: 2011 10th IEEE International Symposium on Mixed and Augmented Reality (ISMAR), pp. 127–136, October 2011Google Scholar
  7. 7.
    Newcombe, R.A., Lovegrove, S., Davison, A.: Dtam: dense tracking and mapping in real-time. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2320–2327, November 2011Google Scholar
  8. 8.
    Salas-Moreno, R., Newcombe, R., Strasdat, H., Kelly, P., Davison, A.: Slam++: Simultaneous localisation and mapping at the level of objects. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1352–1359, June 2013Google Scholar
  9. 9.
    Tzimiropoulos, G., Pantic, M.: Optimization problems for fast aam fitting in-the-wild. In: 2013 IEEE International Conference on Computer Vision (ICCV), pp. 593–600. IEEE (2013)Google Scholar
  10. 10.
    Tzimiropoulos, G., Pantic, M.: Gauss-newton deformable part models for face alignment in-the-wild. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1851–1858 (2014)Google Scholar
  11. 11.
    Zhang, Z.: Iterative point matching for registration of free-form curves and surfaces. International Journal of Computer Vision 13(2), 119–152 (1994)CrossRefGoogle Scholar
  12. 12.
    Zhu, Y., Zhang, Y., Yuille, A.L.: Single image super-resolution using deformable patches. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of LincolnLincolnUK

Personalised recommendations