Conference Paper

Reconstruction of Tree Crown Shape from Scanned Data

DOI: 10.1007/978-3-540-69736-7_79 Conference: Technologies for E-Learning and Digital Entertainment, Third International Conference, Edutainment 2008, Nanjing, China, June 25-27, 2008, Proceedings
Source: DBLP


Reconstruction of a real tree from scattered scanned points is a new challenge in virtual reality. Although many progresses
are made on main branch structures and overall shape of a tree, reconstructions are still not satisfactory in terms of silhouette
and details. We do think that D reconstruction of the tree crown shapes may help to constrain accurate reconstruction of
complete real tree geometry. We propose here a novel approach for tree crown reconstruction based on an improvement of alpha
shape modeling, where the data are points unevenly distributed in a volume rather than on a surface only. The result is an
extracted silhouette mesh model, a concave closure of the input data. We suggest an appropriate scope of proper alpha values,
so that the reconstruction of the silhouette mesh is a valid manifold surface. Experimental results show that our technique
works well in extracting the crown shapes of real trees.

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    ABSTRACT: It is a challenge task to reconstruct a real tree from scattered scanned points in virtual reality. Although many progresses have been made on main branch structures and overall tree shape, reconstructions are still not faithful in terms of silhouette and details. We push the idea that D reconstruction of the tree crown shapes may help to constrain reconstruction of complete real tree geometry. We propose here a new approach to reconstruct tree crown based on clusters of points, where the data are unevenly distributed points in a volume rather than lying on a surface. From this approach several extracted silhouette mesh models can be generated; every mesh model represents a crown section of the reconstructed tree crown. Experimental result shows that our technique works well on crown shape of real trees.
    Plant Growth Modeling, Simulation, Visualization and Applications (PMA), 2009 Third International Symposium on; 01/2009
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    ABSTRACT: Airborne laser scanning (ALS) has become a very common forest inventory data source during the 2000’s. Previous research on single-tree interpretation of such data suggests limitations due to both undetected trees and inaccuracies in species recognition and allometric estimation of stem dimensions. This work examined reconstruction of tree crowns by means of computational geometry of the point data and techniques for turning the obtained crown shape and structure information into improved estimates of tree attributes. Alpha shape metrics, i.e. a collection of various volume, complexity and area features derived from 3-D alpha shapes based on the point data, were found to have potential for describing species-specific allometric differences in the trees, while combining these metrics with features based on the height and intensity distributions in the data was beneficial with respect to the final accuracies. Nearest neighbor estimation proved efficient for making use of the high number of predictors available, but also for the simultaneous estimation of the attributes of interest, thus avoiding error propagation of an estimation chain. Random Forest, in particular, proved to be a flexible method with an ability to handle all available predictors with no need for their reduction. The classification of dominant to intermediate Scots pine, Norway spruce and deciduous trees showed an accuracy of 78%, and the estimates of diameter at breast height, tree height, and stem volume had root mean square errors of 13%, 3%, and 31%, respectively, when evaluated against separate validation data. Less supervised tree detection and estimation resulted in unreliable tree-level descriptions of the test stands, being hindered by both inaccuracy in the tree attributes, especially in species identification, and errors in tree delineation. The need to acquire field reference data and a potential need for an auxiliary information source both place constraints on the applicability of the developed approach. On the other hand, it was shown that crown base height, which is an important measure of external quality of mature Scots pine trees, could be estimated with an RMSE of 20–30% solely by ALS data with a pulse density of 4 m-2. The results suggest focusing single-tree interpretation specifically towards detailed measurements on the dominant tree layer, thus presenting a further need to assess the tree-level production line with respect to obtainable information, alternative methods and their costs.
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    ABSTRACT: Three-dimensional (3D) tree structures play an important role in many scientific fields, including forestry and agriculture. For example, terrestrial laser scanning (TLS) can efficiently capture high-precision 3D spatial arrangements and structure of trees as a point cloud. In the past several methods which reconstruct 3D trees from the TLS point cloud have been proposed. However, in general the previously published methods are not adequate in dealing with incomplete TLS data. To address this problem, a new method that is based on a structure-aware global optimization approach (SAGO) is proposed. The SAGO first obtains the approximate tree skeleton from a distance minimum spanning tree (DMst) and then defines the stretching directions of the branches on the tree skeleton. Based on these stretching directions, the SAGO recovers missing data in the incomplete TLS point cloud. The DMst is applied again to obtain the refined tree skeleton from the optimized data, and the tree skeleton is smoothed by employing a Laplacian transformation function. To reconstruct 3D tree models, the radius of each branch section is estimated, and leaves are added to form the crown geometry. The proposed methodology has been extensively evaluated by employing a dozen TLS point clouds of various types of trees. Both qualitative and quantitative performance evaluation results have indicated that the SAGO is capable to effectively and accurately reconstruct 3D tree models from grossly incomplete TLS point clouds with significant amounts of missing data.
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