SphereFollowing Dijkstra Recursive
Plugin : SimpleForestNom de classe : SF_StepDijkstraLightRecursive
Description
SphereFollowing Dijkstra Recursive - First from an input cloud and an input qsm unfitted points are estimated. Those points are clustered. Each cluster large enough gets processed with the dijkstra algorthim routine after being downscaled. The Dijkstra pathes are used as tree skeleton.
Paramètres
Paramètres de pré-configuration (non modifiables une fois l'étape ajoutée) :
Please excuse potential double citation with the step related citation for the
following general citation for SimpleForest (work on updated citable resource ongoing):
A majority of implementations are based on PCL library:
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For this step please cite in addition the early method using Dijkstra for predicting tree skeleton:
(section 5.1 Tree Allometry)
Paramètres de l'étape :
On each cluster a Dijkstra based tree segmentation is performed. Each point pair with a distance smaller [clustering range] builds a Dijkstra edge.
- Uncheck to deactivate parameterization possibilities of this step. Only recommended for beginners: Activé.
- In beginner mode select point cloud quality: medium.
Please excuse potential double citation with the step related citation for the
following general citation for SimpleForest (work on updated citable resource ongoing):
A majority of implementations are based on PCL library:
------------------------------------------------------------------------------
For this step please cite in addition the early method using Dijkstra for predicting tree skeleton:
(section 5.1 Tree Allometry)
Paramètres de l'étape :
- To even out the distribution and speed things up the cloud is downscaled first to [voxel size] 0.050 (m). .
- All points of the downscaled cloud with a [point to model distance] larger 0.10 (m) are extracted..
- Those unfitted points are clustered with a [clustering range] 0.03 (m). .
On each cluster a Dijkstra based tree segmentation is performed. Each point pair with a distance smaller [clustering range] builds a Dijkstra edge.
Données d'entrée
Structure des données d'entrée recherchées :
Result : QSM result
...
QSM Group (Group)
QSM Item ()
Result : Cloud Result
...
Cloud Group (Group)
Cloud Item (Item with points)
Result : QSM result
...
QSM Group (Group)
QSM Item ()
Result : Cloud Result
...
Cloud Group (Group)
Cloud Item (Item with points)
Références
Hackenberg Jan, Spiecker Heinrich, Calders Kim, Disney Mathias, Raumonen Pasi. 2015. SimpleTree - an efficient open source tool to build tree models from TLS clouds. Multidisciplinary Digital Publishing Institute. Forests.
Hackenberg Jan, Sheppard Jonathan, Spiecker Heinrich, Disney Mathias. 2014. Highly Accurate Tree Models Derived from Terrestrial Laser Scan Data: A Method Description. Multidisciplinary Digital Publishing Institute. Forests.
Rusu Radu Bogdan, Cousins Steve. 2011. 3d is here: Point cloud library (pcl). IEEE. Robotics and Automation (ICRA), 2011 IEEE International Conference on.
Hackenberg Jan, Sheppard Jonathan, Spiecker Heinrich, Disney Mathias. 2014. Highly Accurate Tree Models Derived from Terrestrial Laser Scan Data: A Method Description. Multidisciplinary Digital Publishing Institute. Forests.
Rusu Radu Bogdan, Cousins Steve. 2011. 3d is here: Point cloud library (pcl). IEEE. Robotics and Automation (ICRA), 2011 IEEE International Conference on.