Processing tree point clouds using gaussian mixture models
TL;DR: This paper introduces a method for modelling multi-modal, geometrically complex objects in terrestrial laser scanning point data; specifically, the modelling of trees, which comprises a number of geometric features in conjunction with a multi- modal machine learning technique.
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Abstract: . While traditionally used for surveying and photogrammetric fields, laser scanning is increasingly being used for a wider range of more general applications. In addition to the issues typically associated with processing point data, such applications raise a number of new complications, such as the complexity of the scenes scanned, along with the sheer volume of data. Consequently, automated procedures are required for processing, and analysing such data. This paper introduces a method for modelling multi-modal, geometrically complex objects in terrestrial laser scanning point data; specifically, the modelling of trees. The model method comprises a number of geometric features in conjunction with a multi-modal machine learning technique. The model can then be used for contextually dependent region growing through separating the tree into its component part at the point level. Subsequently object analysis can be performed, for example, performing volumetric analysis of a tree by removing points associated with leaves. The workflow for this process is as follows: isolate individual trees within the scanned scene, train a Gaussian mixture model (GMM), separate clusters within the mixture model according to exemplar points determined by the GMM, grow the structure of the tree, and then perform volumetric analysis on the structure.
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Citations
Terrestrial laser scanning in forest ecology: Expanding the horizon
Kim Calders,Jennifer Adams,John Armston,Harm Bartholomeus,Sébastien Bauwens,Lisa Patrick Bentley,Jérôme Chave,F. Mark Danson,Miro Demol,Miro Demol,Mathias Disney,Rachel Gaulton,Sruthi M. Krishna Moorthy,Shaun R. Levick,Ninni Saarinen,Ninni Saarinen,Crystal B. Schaaf,Atticus E. L. Stovall,Louise Terryn,Phil Wilkes,Hans Verbeeck +20 more
TL;DR: In this article, the authors provide an interdisciplinary focus to explore current developments in terrestrial laser scanning (TLS) to measure and monitor forest structure, and argue that TLS data will play a critical role in understanding fundamental ecological questions about tree size and shape, allometric scaling, metabolic function and plasticity of form.
346
SimpleTree —An Efficient Open Source Tool to Build Tree Models from TLS Clouds
TL;DR: An open source tool, capable of modelling highly accurate cylindrical tree models from terrestrial laser scan point clouds, is presented and evaluated and a global statistical improvement of derived cylinder radii is presented as well as an efficient optimization approach to automatically improve user given input parameters.
314
LiDAR Point Clouds to 3-D Urban Models$:$ A Review
TL;DR: The existing urban reconstruction algorithms, prevalent in computer graphics, computer vision and photogrammetry disciplines, are reviewed, their performance in the architectural modeling context is evaluated, and the adaptability of generic mesh reconstruction techniques to the urban modeling pipeline is discussed.
262
Highly Accurate Tree Models Derived from Terrestrial Laser Scan Data: A Method Description
TL;DR: Using high scan quality data as the input, the resulting models describe the branching structure of the tree, capable of detecting branches with a diameter smaller than a centimeter, showing the high potential of terrestrial laser-scanning for forest inventories.
180
Non Destructive Method for Biomass Prediction Combining TLS Derived Tree Volume and Wood Density
TL;DR: A method for predicting the above ground leafless biomass of trees in a non destructive way by combining volume estimates with density measurements and applying a biomass expansion factor to the biomass of compartments with a diameter larger than 10 cm.
141
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