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Reconstruction of trees from 3D point clouds
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Systems and Control.
2017 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The geometrical structure of a tree can consist of thousands, even millions, of branches, twigs and leaves in complex arrangements. The structure contains a lot of useful information and can be used for example to assess a tree's health or calculate parameters such as total wood volume or branch size distribution. Because of the complexity, capturing the structure of an entire tree used to be nearly impossible, but the increased availability and quality of particularly digital cameras and Light Detection and Ranging (LIDAR) instruments is making it increasingly possible.

A set of digital images of a tree, or a point cloud of a tree from a LIDAR scan, contains a lot of data, but the information about the tree structure has to be extracted from this data through analysis.

This work presents a method of reconstructing 3D models of trees from point clouds. The model is constructed from cylindrical segments which are added one by one. Bayesian inference is used to determine how to optimize the parameters of model segment candidates and whether or not to accept them as part of the model. A Hough transform for finding cylinders in point clouds is presented, and used as a heuristic to guide the proposals of model segment candidates.

Previous related works have mainly focused on high density point clouds of sparse trees, whereas the objective of this work was to analyze low resolution point clouds of dense almond trees. The method is evaluated on artificial and real datasets and works rather well on high quality data, but performs poorly on low resolution data with gaps and occlusions.

Place, publisher, year, edition, pages
2017. , p. 62
Series
UPTEC F, ISSN 1401-5757 ; 17011
Keywords [en]
Bayes, Bayesian inference, point cloud, pointcloud, 3D, tree, trees, reconstruction, Matlab, Povray, Arbaro, model, LIDAR, Hough transform, cylinder, geometry, heuristic, Australia, Australian Centre for Field Robotics, spherical grid, icosahedron, likelihood, intersection, artificial data, color encoded, depth map, coordinate map, ray tracing
National Category
Information Systems Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:uu:diva-316833OAI: oai:DiVA.org:uu-316833DiVA, id: diva2:1079140
Educational program
Master Programme in Engineering Physics
Presentation
2014-10-23, Link Building, 142-144 Shepherd St Darlington NSW 2008, Australia, Sydney, Australia, 10:00 (English)
Supervisors
Examiners
Available from: 2017-03-08 Created: 2017-03-07 Last updated: 2018-01-13Bibliographically approved

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