Abstract
Accurate 3D reconstruction of morphological structures, gro- wth processes is a prerequisite for image-based measurement of biological organisms. It is a particular challenge for digital plant and crop research. In this paper, multi-view images of structurally very different plants including Arabidopsis, Wheat and Physalis, were taken with a consumer-grade digital camera with a zoom lens and a turntable. Camera parameters were estimated using the SfM method (COLMAP), and then 3D point clouds were reconstructed using MVSNet and the camera parameters. The results show that the proposed method is able to quickly produce denser and complete 3D point cloud of plants. Compared with the existing methods, our method is an end-to-end framework and is more automatic and more promising for dense 3D reconstruction of plants, especially for plant phenotyping.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Zeng, L.L., Zhang, L.L., Yang, Y., et al.: 3D plants reconstruction based on point cloud. Int. J. Perform. Eng. 14(1), 121–133 (2018)
Schonberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: IEEE Conference on Computer Vision & Pattern Recognition, pp. 4104–4113. IEEE (2016)
Yao, Y., Luo, Z., Li, S., et al.: MVSNet: depth inference for unstructured multi-view stereo. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 767–783 (2018)
Reffye, P.D., Edelin, C., Francon, J.M., et al.: Plants models faithful to botanical structure and development. ACM Siggraph Comput. Gr. 22(4), 151–158 (2011)
Okabe, M., Owada, S., et al.: Interactive design of botanical trees using freehand sketches and example-based editing. In: Proceeding of EUROGRAPHICS, vol. 24, no. 3 (2005)
Shlyakhter, I., Rozenoer, M., Dorsey, J., et al.: Reconstructing 3D tree models from instrumented photographs. IEEE Comput. Gr. Appl. 21, 53–61 (2001)
Reche, A., Martin, I., Drettakis, G.: Volumetric reconstruction and interactive rendering of trees from photographs. ACM Trans. Gr. 23(3), 720–727 (2004)
Quan, L., et al.: Image-based plant modeling. ACM Trans Graphic (2006)
Lhuillier, M., Quan, L., Lhuillier, M., et al.: A quasi-dense approach to surface reconstruction from uncalibrated images. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 418–433 (2005)
Tan, P., et al.: Image-based tree modeling. TOG 26(3), 87 (2007)
Paproki, A., Sirault, X., Berry, S., et al.: A novel mesh processing based technique for 3D plant analysis. BMC Plant Biol. 12(1), 63 (2012)
Miller, J., Morgenroth, J., Gomez, C.: 3D modelling of individual trees using a handheld camera: accuracy of height, diameter and volume estimates. Urban Forestry Urban Green. 14(4), 932–940 (2015)
Hui, F., Zhu, J.Y., Hu, P.C., et al.: lmage-based dynamic quantification and high-accuracy 3D evaluation of canopy structure of plant populations. Ann. Bot. 121(5), 1079–1088 (2018)
Livny, F., Yan, F., et al.: Automatic reconstruction of tree skeletal structures from point clouds. ACM Trans. Gr. 81–95 (2010)
Lou, L.: Cost-effective accurate 3-D reconstruction based on multi-view images for plant phenotyping (2016)
Hartley, R.I., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press, Cambridge (2004)
Merrell, P., Akbarzadeh, A., Wang, L., et al.: Real-time visibility-based fusion of depth maps. In: International Conference on Computer Vision (ICCV), Rio de Janeiro (2007)
Barnes, C., et al.: The generalized patchmatch correspondence algorithm. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) Computer Vision—ECCV 2010. ECCV 2010. LNCS, vol. 6313. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15558-1_3
Ghahremani, M., Williams, K., Tiddeman, B., et al.: Deep segmentation of point clouds of wheat. Front. Plant Sci. 12, 608732 (2016)
Acknowledgements
We acknowledge funding from BBSRC NCG Grant Ref: BBS/E/W/0012844A and the Technological Innovation Project of Postgraduate Student of Chongqing (No. 2021S0049). We would like to thank the members of the NPPC of IBERS of Aberystwyth University for providing plant material and help on image acquisition.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Chen, Z., Lv, H., Lou, L., Doonan, J.H. (2022). Fast and Accurate 3D Reconstruction of Plants Using MVSNet and Multi-View Images. In: Jansen, T., Jensen, R., Mac Parthaláin, N., Lin, CM. (eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing, vol 1409. Springer, Cham. https://doi.org/10.1007/978-3-030-87094-2_34
Download citation
DOI: https://doi.org/10.1007/978-3-030-87094-2_34
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87093-5
Online ISBN: 978-3-030-87094-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)