Skip to main content
SpringerLink
Log in
Book cover

Asian Conference on Computer Vision

ACCV 2016: Computer Vision – ACCV 2016 Workshops pp 222–235Cite as

Radiometry Propagation to Large 3D Point Clouds from Sparsely Sampled Ground Truth

  • Conference paper
  • First Online:

  • 2015 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10117))

Abstract

Good radiometry of a 3D reconstruction is essential for digital conservation and versatile visualization of cultural heritage artifacts and sites. For large sites, “true” radiometry for the complete 3D point cloud is very expensive to obtain. We present a method that is capable to reconstruct the radiometric surface properties of an entire scene despite the fact that we only have access to the “true” radiometry of a small part of it. This is done in a two stage process: First, we transfer the radiometry to spatially corresponding parts of the scene, and second, we propagate these values to the entire scene using affinity information. We apply our method to 3D point clouds and 2D images, and show excellent quantitative and visually pleasing qualitative results. This approach can be of high value in many applications where users want to improve phototextured models towards high-quality yet affordable radiometry.

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

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 EPUB and 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

Notes

  1. 1.

    We thank the authors of [1] for providing their data, and use our own high-resolution, “true” radiometry data [16].

References

  1. Mostegel, C., Rumpler, M., Fraundorfer, F., Bischof, H.: UAV-based autonomous image acquisition with multi-view stereo quality assurance by confidence prediction. In: Proceedings of CVPR Workshop (2016)

    Google Scholar 

  2. Agarwal, S., Furukawa, Y., Snavely, N., Simon, I., Curless, B., Seitz, S., Szeliski, R.: Building rome in a day. Commun. ACM 54, 105–111 (2011)

    Article  Google Scholar 

  3. Rothermel, M., Wenzel, K., Fritsch, D., Haala, N.: SURE: photogrammetric surface reconstruction from imagery. In: Proceedings of LC3D Workshop (2012)

    Google Scholar 

  4. Furukawa, Y., Ponce, J.: Accurate, dense, and robust multiview stereopsis. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1362–1376 (2010)

    Article  Google Scholar 

  5. Reinhard, E., Adhikhmin, M., Gooch, B., Shirley, P.: Color transfer between images. IEEE Comput. Graph. Appl. 21, 34–41 (2001)

    Article  Google Scholar 

  6. Ruderman, D.L., Cronin, T.W., Chiao, C.C.: Statistics of cone responses to natural images: implications for visual coding. J. Opt. Soc. Am. A 15, 2036–2045 (1998)

    Article  Google Scholar 

  7. Pitié, F., Kokaram, A.C., Dahyot, R.: N-dimensional probablility density function transfer and its application to colour transfer. In: Proceedings of ICCV (2005)

    Google Scholar 

  8. Pitié, F., Kokaram, A.C., Dahyot, R.: Automated colour grading using colour distribution transfer. Comput. Vis. Image Underst. 107, 123–137 (2007)

    Article  Google Scholar 

  9. Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall Inc., Upper Saddle River (2006)

    Google Scholar 

  10. Rabin, J., Peyré, G., Delon, J., Bernot, M.: Wasserstein barycenter and its application to texture mixing. In: Bruckstein, A.M., Haar Romeny, B.M., Bronstein, A.M., Bronstein, M.M. (eds.) SSVM 2011. LNCS, vol. 6667, pp. 435–446. Springer, Heidelberg (2012). doi:10.1007/978-3-642-24785-9_37

    Chapter  Google Scholar 

  11. Villani, C.: Topics in Optimal Transportation. Graduate Studies in Mathematics. American Mathematical Society, cop., Providence (2003)

    Book  MATH  Google Scholar 

  12. Rubner, Y., Tomasi, C., Guibas, L.J.: A metric for distributions with applications to image databases. In: Proceedings of ICCV (1998)

    Google Scholar 

  13. Shirdhonkar, S., Jacobs, D.W.: Approximate earth mover’s distance in linear time. In: Proceedings of CVPR (2008)

    Google Scholar 

  14. Bottou, L.: Online algorithms and stochastic approximations. In: Saad, D. (ed.) Online Learning and Neural Networks. Cambridge University Press, Cambridge (1998). Revised, October 2012

    Google Scholar 

  15. Zhu, X., Ghahramani, Z.: Learning from labeled and unlabeled data with label propagation. Technical report (2002)

    Google Scholar 

  16. Höll, T., Pinz, A.: Cultural heritage acquisition: geometry-based radiometry in the wild. In: Proceedings of 3D Vision (3DV) (2015)

    Google Scholar 

  17. Gehler, P.V., Rother, C., Blake, A., Minka, T., Sharp, T.: Bayesian color constancy revisited. In: Proceedings of CVPR (2008)

    Google Scholar 

  18. Lynch, S.E., Drew, M.S., Finlayson, G.D.: Colour constancy from both sides of the shadow edge. In: Proceedings of ICCV Workshop (2013)

    Google Scholar 

  19. Wolf, S.: Color correction matrix for digital still and video imaging systems. Technical report TM-04-406, National Telecommunications and Information Administration, Washington D.C. (2003)

    Google Scholar 

  20. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13, 600–612 (2004)

    Article  Google Scholar 

Download references

Acknowledgements

The research leading to these results was partly funded by the EC FP7 project 3D-PITOTI (ICT-2011-600545). The colourful painting in Sect. 4.2 is a 3D scan of a reproduction of a painting by August Macke. We thank ArcTron 3D GmbH (http://www.arctron.de) for providing us the data. We thank the Institute for Computer Graphics and Vision (ICG, TU Graz) for providing us the large-scale 3D reconstruction for our experiments in Sect. 4.1. We also thank MiBACT-SBA Lombardia and the Parco Archeologico Comunale di Seradina-Bedolina for permission to scan at Seradina I rock 12C. We appreciate the permission to use an academic license of the SURE software package [3] for dense 3D reconstruction.

Author information

Authors and Affiliations

  1. Institute of Electrical Measurement and Measurement Signal Processing, Graz University of Technology, Graz, Austria

    Thomas Höll & Axel Pinz

Authors
  1. Thomas Höll
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Axel Pinz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thomas Höll .

Editor information

Editors and Affiliations

  1. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Chu-Song Chen

  2. Tsinghua University, Beijing, China

    Jiwen Lu

  3. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Kai-Kuang Ma

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Höll, T., Pinz, A. (2017). Radiometry Propagation to Large 3D Point Clouds from Sparsely Sampled Ground Truth. In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. ACCV 2016. Lecture Notes in Computer Science(), vol 10117. Springer, Cham. https://doi.org/10.1007/978-3-319-54427-4_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-54427-4_17

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54426-7

  • Online ISBN: 978-3-319-54427-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation