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Force field driven skeleton extraction method for point cloud trees

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Abstract

A key step in processing natural trees from point cloud data is to reconstruct the trees’ skeleton, which plays an important role in forest investigation and monitoring. Although the techniques for general objects skeletonizing based on point clouds have made a large stride, there are few efficient and simple studies on the natural trees, which have complex topologies. In this paper, we propose a novel method to reconstruct tree skeletons based on the point cloud data, named as the force field driven tree skeleton extracting method, which consists of the following steps. Specifically, to make the point cloud tree a little bit cleaner, the hierarchical subdivision to the original point cloud space is firstly proposed. Then the split-level of the trees’ applied space is considered in each layers, and a simplified representation of the feature points for the tree model is then established under the neighbor relationships. After that, the feature points in the peripheral are connected by the geodesic distance. To get the initial skeleton, the surface geodesic lines are compressed into the tree model by applying a visible repulsive force field. Finally, the final skeleton is acquired by polishing the initial skeleton according to a threshold setting. The experimental results on two kinds of representative naturally growing trees, which are the Cherry and Michelia, indicate that this method can provide a satisfactory performance.

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References

  • Aiteanu F, Klein R (2014) Hybrid tree reconstruction from inhomogeneous point clouds. Vis Comput 30:763–771

    Article  Google Scholar 

  • Amenta N, Bern M, Kamvysselis M (1998) A new voronoi-based surface reconstruction algorithm. In: Proceedings of the 25th annual conference on computer graphics and interactive techniques. ACM, pp 415–421

  • Amenta N, Choi S, Kolluri RK (2001) The power crust. In: Proceedings of the sixth ACM symposium on solid modeling and applications. ACM, pp 249–266

  • An Y, Li Z, Shao C (2013) Feature extraction from 3d point cloud data based on discrete curves. Math Probl Eng 2013:1–19

    Google Scholar 

  • Balzer J, Vincze M (2016) Modeling connected regions in arbitrary planar point clouds by robust B-spline approximation. North-Holland Publishing Co

  • Bradshaw G, Osullivan C (2004) Adaptive medial-axis approximation for sphere-tree construction. ACM Trans Graph 23(1):1– 26

    Article  Google Scholar 

  • Bucksch A, Lindenbergh RC, Menenti M (2010) Skeltre: robust skeleton extraction from imperfect point clouds. Vis Comput 26(10):1283–1300

    Article  Google Scholar 

  • Burt A, Disney M, Raumonen P, et al. (2013) Rapid characterisation of forest structure from tls and 3d modelling. In: Proceedings international geoscience and remote sensing symposium (IGARSS 2013), pp 3387–3390

  • Chu C-T, Chiang H-K, Chang Y-S (2016) Clustering algorithms applied gaussian basis function neural network compensator with fuzzy control for magnetic bearing system. In: 2016 the 5th international symposium on next-generation electronics (ISNE). IEEE, pp 1–2

  • Deliormanli AH, Maerz NH, Otoo J (2014) Using terrestrial 3d laser scanning and optical methods to determine orientations of discontinuities at a granite quarry. Int J Rock Mech Mining Sci 66:41–48

    Article  Google Scholar 

  • Djuricic A, Puttonen E, Harzhauser M, et al. (2016) 3d central line extraction of fossil oyster shells. ISPRS Ann Photogrammetry Remote Sens Spatial Inf Sci 3(5)

  • Dong Z, Ting Y, Xue L, et al. (2015) Skeleton extraction for real trees. Int J Advancements Comput Technol 7(4):22

    Google Scholar 

  • Ducey MJ, Astrup R (2013) Adjusting for nondetection in forest inventories derived from terrestrial laser scanning. Can J Remote Sens 39(5):410–425

    Google Scholar 

  • Eckart BD, Kim K, Troccoli AJ et al (2016) Modeling point cloud data using hierarchies of gaussian mixture models. US Patent App. 15/055,440

  • Gottschalk S, Lin MC, Manocha D (1996) Obbtree: a hierarchical structure for rapid interference detection. In: International conference on computer graphics and interactive techniques, pp 171–180

  • Huang H, Wu S, Cohenor D, et al. (2013) L 1 -medial skeleton of point cloud. Int Conf Comput Graph Interact Techn 32(4):65

    Google Scholar 

  • Kandare K, Orka HO, Dalponte M, et al. (2017) Individual tree crown approach for predicting site index in boreal forests using airborne laser scanning and hyperspectral data. Int J Appl Earth Observ Geoinform 60:72–82

    Article  Google Scholar 

  • Kankare V, Puttonen E, Holopainen M, et al. (2016) The effect of tls point cloud sampling on tree detection and diameter measurement accuracy. Remote Sens Lett 7(5):495–502

    Article  Google Scholar 

  • Kim J, Kim D, Cho H (2013) Procedural modeling of trees based on convolution sums of divisor functions for real-time virtual ecosystems. Comput Anim Virtual Worlds 24:237–246

    Article  Google Scholar 

  • Lamb SM, MacLean DA, Hennigar CR, et al. (2018) Forecasting forest inventory using imputed tree lists for lidar grid cells and a tree-list growth model. Forests 9(4):165

    Article  Google Scholar 

  • Liao Q, Guan N, Zhangg Q (2016) Gauss-seidel based non-negative matrix factorization for gene expression clustering. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2364–2368

  • Lidayov K, Frimmel H, Wang C et al (2016) Fast vascular skeleton extraction algorithm

  • Lindberg F (2016) Peer review report on “retrieval of three-dimensional tree canopy and shade using terrestrial laser scanning (tls) data to analyze the cooling effect of vegetation”. Agr Forest Meteorol 9(217):125

    Article  Google Scholar 

  • Livny Y, Yan F, Olson M, et al. (2010) Automatic reconstruction of tree skeletal structures from point clouds. Int Conf Comput Graph Interact Tech 29(6):151

    Google Scholar 

  • Maltamo M, Bollandss OM, Gobakken T, et al. (2016) Large-scale prediction of aboveground biomass in heterogeneous mountain forests by means of airborne laser scanning. Can J Forest Res 46(9):1138–1144

    Article  Google Scholar 

  • Mikita T, Janata P, Surovy P (2016) Forest stand inventory based on combined aerial and terrestrial close-range photogrammetry. Forests 7(8):165

    Article  Google Scholar 

  • Shlyakhter I, Rozenoer M, Dorsey J, et al. (2001) Reconstructing 3d tree models from instrumented photographs. IEEE Comput Graph Appl 21(3):53–61

    Article  Google Scholar 

  • Su J, Bai H, Zhang P, et al. (2016) Intentional islanding algorithm for distribution network based on layered directed tree model. Energies 9(3):1–21

    Article  Google Scholar 

  • Su Z, Zhao Y, Zhao C, et al. (2011) Skeleton extraction for tree models. Math Comput Model 54 (3):1115–1120

    Article  Google Scholar 

  • Urbach JM (2014) Efficiently implementing and displaying independent 3-dimensional interactive viewports of a virtual world on multiple client devices. US Patent 8,817, 025

  • Wallace L, Lucieer A, Malenovsky Z, et al. (2016) Assessment of forest structure using two uav techniques: a comparison of airborne laser scanning and structure from motion (sfm) point clouds. Forests 7(3):1–16

    Google Scholar 

  • Wang C, Han D, Yang K (2008) L-system modeling based on trees’ synthetic characteristics. In: International symposium on computational intelligence and design, pp 87–90

  • Wang Z, Zhang L, Fang T, et al. (2014) A structure-aware global optimization method for reconstructing 3-d tree models from terrestrial laser scanning data. IEEE Trans Geosci Remote Sens 52(9):5653–5669

    Article  Google Scholar 

  • Wang Y, Hyyppa J, Liang X, et al. (2016) International benchmarking of the individual tree detection methods for modeling 3-d canopy structure for silviculture and forest ecology using airborne laser scanning. IEEE Trans Geosci Remote Sens 54(9):5011–5027

    Article  Google Scholar 

  • Wu J, Zhang G, Xia J, et al. (2010) Gray cerebrovascular image skeleton extraction algorithm using level set model. J Multimed 5(3):208–215

    Google Scholar 

  • Xie K, Yan F, Sharf A, et al. (2016) Tree modeling with real tree-parts examples. IEEE Trans Visual Comput Graph 22(12):2608–2618

    Article  Google Scholar 

  • Zhang D, Liu J, Xue L, et al. (2016a) Tree canopy image segmentation based on ncspso-afsa optimization of svm. J Northwest Agri Sci Technol Univ: Natural Sci Edn 44(3):118–124

  • Zhang D, Yan R, Yun T, et al. (2016b) The modeling method based on the ground laser radar chanmu branches. J Forest Eng 1(5):107–114

  • Zhou G, Wang B, Zhou J (2014) Automatic registration of tree point clouds from terrestrial lidar scanning for reconstructing the ground scene of vegetated surfaces. IEEE Geosci Remote Sens Lett 11(9):1654–1658

    Article  Google Scholar 

  • Zhou G, Cao S, Sun Z (2015) Automatic registration of tree point clouds from terrestrial laser scanning. In: 2015 IEEE international, geoscience and remote sensing symposium (IGARSS). IEEE, pp 561–564

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (31670554), the Natural Science Foundation of Jiangsu Province (BK20161527), the Central Public-interest Scientific Institution Basal Research Fund (CAFYBB2016SZ003) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX17_0819).

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Authors and Affiliations

  1. College of Information Science and Technology, Nanjing Forestry University, Longpan NO. 159, Nanjing, 210037, China

    Linming Gao

  2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Xiaolingwei NO. 200, Nanjing, 210094, China

    Dong Zhang

  3. College of Biology and Environment Sciences, Nanjing Forestry University, Longpan NO. 159, Nanjing, 210037, China

    Nan Li

  4. College of Science, Nanjing Forestry University, Longpan NO. 159, Nanjing, 210037, China

    Lei Chen

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  1. Linming Gao
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  2. Dong Zhang
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  3. Nan Li
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Correspondence to Linming Gao.

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Communicated by: H. A. Babaie

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Gao, L., Zhang, D., Li, N. et al. Force field driven skeleton extraction method for point cloud trees. Earth Sci Inform 12, 161–171 (2019). https://doi.org/10.1007/s12145-018-0365-3

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