A Cost-Effective Automatic 3D Reconstruction Pipeline for Plants Using Multi-view Images

  • Lu Lou
  • Yonghuai Liu
  • Minglan Sheng
  • Jiwan Han
  • John H. Doonan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8717)

Abstract

Plant phenotyping involves the measurement, ideally objectively, of characteristics or traits. Traditionally, this is either limited to tedious and sparse manual measurements, often acquired destructively, or coarse image-based 2D measurements. 3D sensing technologies (3D laser scanning, structured light and digital photography) are increasingly incorporated into mass produced consumer goods and have the potential to automate the process, providing a cost-effective alternative to current commercial phenotyping platforms. We evaluate the performance, cost and practicability for plant phenotyping and present a 3D reconstruction method from multi-view images acquired with a domestic quality camera. This method consists of the following steps: (i) image acquisition using a digital camera and turntable; (ii) extraction of local invariant features and matching from overlapping image pairs; (iii) estimation of camera parameters and pose based on Structure from Motion(SFM); and (iv) employment of a patch based multi-view stereo technique to implement a dense 3D point cloud. We conclude that the proposed 3D reconstruction is a promising generalized technique for the non-destructive phenotyping of various plants during their whole growth cycles.

Keywords

Phenotyping multi-view images structure from motion multi-view stereo 3D reconstruction 

References

  1. 1.
    Alcantarilla, P.F., Nuevo, J., Bartoli, A.: Fast explicit diffusion for accelerated features in nonlinear scale spaces. In: British Machine Vision Conf., BMVC (2013)Google Scholar
  2. 2.
    Biskup, B., Scharr, H., Rascher, U.S.: A stereo imaging system for measuring structural parameters of plant canopies. Plant, Cell & Environment 30 (2007)Google Scholar
  3. 3.
    Bay, H., Ess, A., Tuytelaars, T., Gool, L.V.: Speeded-up robust features (surf). Comput. Vis. Image Underst. 110, 346–359 (2008)CrossRefGoogle Scholar
  4. 4.
    Cobb, J.N., DeClerck, G., Greenberg, A., Clark, R., McCouch, S.: Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype-phenotype relationships and its relevance to crop improvement. Theoretical and Applied Genetics 126, 867–887 (2013)CrossRefGoogle Scholar
  5. 5.
    Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Andwu, A.Y.: An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J. of the ACM 45(6), 891–923 (1998)CrossRefMATHGoogle Scholar
  6. 6.
    Fischler, M.A., Bolles, R.C.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)CrossRefMathSciNetGoogle Scholar
  7. 7.
    Stewenius, H., Engels, C., Nister, D.: Recent developments on direct relative orientation. ISPRS Journal of Photogrammetry and Remote Sensing 60, 284–294 (2006)CrossRefGoogle Scholar
  8. 8.
    Furukawa, Y., Ponce, J.: Accurate, dense, and robust multiview stereopsis. IEEE Tran. PAMI 32, 1362–1376 (2010)CrossRefGoogle Scholar
  9. 9.
    Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press (2004)Google Scholar
  10. 10.
    Hern, C., Esteban, N., Schmitt, F.: Silhouette and stereo fusion for 3d object modeling. Comput. Vis. Image Underst. 96, 367–392 (2004)CrossRefGoogle Scholar
  11. 11.
    Ivanov, N., et al.: Computer stereo plotting for 3-d reconstruction of a maize canopy. Agricultural and Forest Meteorology 75, 85–102 (1995)CrossRefGoogle Scholar
  12. 12.
    Jancosek, M., Pajdla, T.: Multi-view reconstruction preserving weakly-supported surfaces. In: Proc. CVPR, pp. 3121–3128 (2011)Google Scholar
  13. 13.
    Kaminuma, E., Heida, N., Tsumoto, Y., Yamamoto, N., Goto, N., Okamoto, N., Konagaya, A., Matsui, M., Toyoda, T.: Automatic quantification of morphological traits via three-dimensional measurement of arabidopsis. Plant 38, 358–365 (2004)CrossRefGoogle Scholar
  14. 14.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)CrossRefGoogle Scholar
  15. 15.
    Morel, J.M., Yu, G.: Asift: A new framework for fully affine invariant image comparison. SIAM J. Img. Sci. 2(2), 438–469 (2009)CrossRefMATHMathSciNetGoogle Scholar
  16. 16.
    Organisation, E.P.S.: White paper of plant phenotyping. Tech. rep. (2010)Google Scholar
  17. 17.
    Paproki, A., Sirault, X., Berry, S., Furbank, R., Fripp, J.: A novel mesh processing based technique for 3d plant analysis. BMC Plant Biology 12 (2012)Google Scholar
  18. 18.
    Prince, S.J.D.: Computer Vision: Models, Learning, and Inference. Cambridge University Press (2012)Google Scholar
  19. 19.
    Quan, L., Tan, P., Zeng, G., Yuan, L., Wang, J.D., Kang, S.B.: Image-based plant modeling. ACM Trans. Graphics 25, 599–604 (2006)CrossRefGoogle Scholar
  20. 20.
    Seitz, S.M., Curless, B., Diebel, J., Scharstein, D., Szeliski, R.: A comparison and evaluation of multi-view stereo reconstruction algorithms. In: Proc. CVPR, vol. 1, pp. 519–528 (2006)Google Scholar
  21. 21.
    Snavely, N., Seitz, S.M., Szeliski, R.: Photo tourism: Exploring photo collections in 3d. In: Proc. SIGGRAPH, pp. 835–846 (2006)Google Scholar
  22. 22.
    Stephens, C.H.: Mike: A combined corner and edge detector. In: Proc. of Fourth Alvey Vision Conference, pp. 5–10 (1988)Google Scholar
  23. 23.
    Santos, T., Oliveira, A.: Image-based 3d digitizing for plant architecture analysis and phenotyping. In: Proc. Workshop on Industry Applications in SIB-GRAPI (2012)Google Scholar
  24. 24.
    Tola, E., Lepetit, V., Fua, P.: Daisy: An efficient dense descriptor applied to wide-baseline stereo. IEEE Tran. PAMI 32(5), 815–830 (2010)CrossRefGoogle Scholar
  25. 25.
    Vogiatzis, G., Hern, C., Esteban, N., Torr, P.H.S., Cipolla, R.: Multiview stereo via volumetric graph-cuts and occlusion robust photo-consistency. IEEE Trans. Pattern Anal. Mach. Intell. 29, 2241–2246 (2007)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Lu Lou
    • 1
    • 2
  • Yonghuai Liu
    • 1
  • Minglan Sheng
    • 2
  • Jiwan Han
    • 1
  • John H. Doonan
    • 1
  1. 1.Department of Computer ScienceAberystwyth UniversityAberystwythUK
  2. 2.College of Information Science and EngineeringChongqing Jiaotong UniversityChongqingChina

Personalised recommendations