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3D Reconstruction Under Weak Illumination Using Visibility-Enhanced LDR Imagery

  • Nader H. AldeebEmail author
  • Olaf Hellwich
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 943)

Abstract

Images of objects captured under poor illumination conditions inevitably contain noise and under-exposed regions where important geometric features may be hidden. Using these images for 3D reconstruction may impair the quality of the generated models. To improve 3D reconstruction under poor illumination, this paper proposes a simple solution for reviving buried features in dark images before feeding them into 3D reconstruction pipelines. Nowadays, many approaches for improving the visibility of details in dark images exist. However, according to our knowledge, none of them fulfills the requirements for a successful 3D reconstruction. Proposed approach in this paper aims not only to enhance the visibility but also contrast of features in dark images. Experiments conducted using challenging datasets of dark images demonstrate a significant improvement of generated 3D models in terms of visibility, completeness, and accuracy. It also shows that the proposed methodology outperforms state-of-the-art approaches that tackle the same problem.

Keywords

Dark image Photogrammetry Visibility enhancement 

Notes

Acknowledgment

The authors would like to thank the German Academic Exchange Service (DAAD) for supporting this research.

References

  1. 1.
    Arias, P., Ordóñez, C., Lorenzo, H., Herraez, J., Armesto, J.: Low-cost documentation of traditional agro-industrial buildings by close-range photogrammetry. Build. Environ. 42(4), 1817–1827 (2007)CrossRefGoogle Scholar
  2. 2.
    Kim, J.-M., Shin, D.-K., Ahn, E.-Y.: Image-based modeling for virtual museum. In: Multimedia, Computer Graphics and Broadcasting, pp. 108–119. Springer (2011)Google Scholar
  3. 3.
    Ali, M.J., Naik, M.N., Kaliki, S., Dave, T.V., Dendukuri, G.: Interactive navigation-guided ophthalmic plastic surgery: the techniques and utility of 3-dimensional navigation. Can. J. Ophthalmol./J. Can. d’Ophtalmologie 52(3), 250–257 (2017)CrossRefGoogle Scholar
  4. 4.
    Nocerino, E., Lago, F., Morabito, D., Remondino, F., Porzi, L., Poiesi, F., Rota Bulo, S., Chippendale, P., Locher, A., Havlena, M., et al.: A smartphone-based 3D pipeline for the creative industry-the replicate EU project. 3D Virtual Reconstr. Vis. Complex Arch. 42(W3), 535–541 (2017)Google Scholar
  5. 5.
    Remondino, F., El-Hakim, S.: Image-based 3D modelling: a review. Photogramm. Rec. 21(115), 269–291 (2006)CrossRefGoogle Scholar
  6. 6.
    Aguilera, D.G., Lahoz, J.G.: Laser scanning or image-based modeling? A comparative through the modelization of San Nicolas Church. In: Proceedings of ISPRS Commission V Symposium of Image Engineering and Vision Metrology (2006)Google Scholar
  7. 7.
    Wu, C.: SiftGPU: a GPU implementation of scale invariant feature transform (sift) (2007). http://cs.unc.edu/~ccwu/siftgpu
  8. 8.
    Wu, C.: Towards linear-time incremental structure from motion. In: International Conference on 3D Vision - 3DV 2013, pp. 127–134 (2013)Google Scholar
  9. 9.
    Wu, C., Agarwal, S., Curless, B., Seitz, S.M.: Multicore bundle adjustment. In: Computer Vision and Pattern Recognition, pp. 3057–3064 (2011)Google Scholar
  10. 10.
    Furukawa, Y., Ponce, J.: Accurate, dense, and robust multi-view stereopsis. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1362–1376 (2010)CrossRefGoogle Scholar
  11. 11.
    Ying, Z., Li, G., Ren, Y., Wang, R., Wang, W.: A new image contrast enhancement algorithm using exposure fusion framework. In: International Conference on Computer Analysis of Images and Patterns, pp. 36–46. Springer (2017)Google Scholar
  12. 12.
    Guo, X., Li, Y., Ling, H.: LIME: low-light image enhancement via illumination map estimation. IEEE Trans. Image Process. 26(2), 982–993 (2017)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Li, L., Wang, R., Wang, W., Gao, W.: A low-light image enhancement method for both denoising and contrast enlarging. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 3730–3734. IEEE (2015)Google Scholar
  14. 14.
    Shi, Z., Mei Zhu, M., Guo, B., Zhao, M., Zhang, C.: Nighttime low illumination image enhancement with single image using bright/dark channel prior. EURASIP J. Image Video Process. 2018(1), 13 (2018)CrossRefGoogle Scholar
  15. 15.
    Forsyth, D.A.: A novel algorithm for color constancy. Int. J. Comput. Vis. 5(1), 5–35 (1990)CrossRefGoogle Scholar
  16. 16.
    Funt, B., Shi, L.: The rehabilitation of MaxRGB. In: Color and Imaging Conference, vol. 1, pp. 256–259. Society for Imaging Science and Technology (2010)Google Scholar
  17. 17.
    Joze, H.R.V., Drew, M.S., Finlayson, G.D., Rey, P.A.T.: The role of bright pixels in illumination estimation. In: Color and Imaging Conference, vol. 1, pp. 41–46. Society or Imaging Science and Technology (2012)Google Scholar
  18. 18.
    Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics Gems IV, pp. 474–485. Academic Press Professional, Inc. (1994)Google Scholar
  19. 19.
    Sanaee, P., Moallem, P., Razzazi, F.: A structural refinement method based on image gradient for improving performance of noise-restoration stage in decision based filters. Digit. Signal Process. 75, 242–254 (2018)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Aldeeb, N.H., Hellwich, O.: Reconstructing textureless objects - image enhancement for 3D reconstruction of weakly-textured surfaces. In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP, vol. 5, pp. 572–580. INSTICC, SciTePress (2018)Google Scholar
  21. 21.
    Jensen, R., Dahl, A., Vogiatzis, G., Tola, E., Aanæs, H.: Large scale multi-view stereopsis evaluation. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 406–413. IEEE (2014)Google Scholar
  22. 22.
    Cloudcompare, version 2.10, GPL software. http://www.cloudcompare.org/. Accessed 15 Sept 2018

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Computer Vision and Remote SensingTechnische Universität BerlinBerlinGermany

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