SphereFollowing
Plugin : SimpleForestNom de classe : SF_StepSpherefollowingBasic
Description
SphereFollowing - This implementation of the SphereFollowing method utilizes an unsegmented tree cloud. On the sphere following parameters a raster search if performed.
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 paper presenting the spherefollowing routine:
(section 4.3. Cylinder Model Creation)
And this presenting the automatic parameter search by using cloud to model distance for QSM modeling:
(section 2.2. Tree Modeling - Parameter Optimization)
Paramètres de l'étape :
Pre Processing:
SphereFollowing Method Optimizationable Parameters:
Cloud To Model Distance:
For the grid search evaluation the parameter set with the smallest qsm to cloud distance is chosen.
SphereFollowing Method Hyper Parameters:
First Hyper Parameters are set. Those parameters will never change during optimization.
- 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 paper presenting the spherefollowing routine:
(section 4.3. Cylinder Model Creation)
And this presenting the automatic parameter search by using cloud to model distance for QSM modeling:
(section 2.2. Tree Modeling - Parameter Optimization)
Paramètres de l'étape :
Pre Processing:
- To even out the distribution and speed things up the cloud is downscaled first to [voxel size] 0.010 (m). .
- Only the largest cluster will be processed with [clustering range] 0.10 (m). .
SphereFollowing Method Optimizationable Parameters:
- You can let Simple Forest choose the following method parameters: Activé select if you want [auto parameters].
- To generate a sphere each fitted circle is multiplied with the [sphere multiplier]: 2.00 ..
- We search for sphere multiplier with the following potential parameterization: 75; 100; 150; 200; 250 %..
- The sphere surface has a thickness of [sphere epsilon] 0.01 (m)..
- We search for sphere epsilon with the following potential parameterization: 75; 100; 150; 200; 250 %..
- Point on sphere surface are clustered with threshold [euclidean clustering distance] 0.03 (m) to build new circle clusters..
- We search for euclidean clustering distance with the following potential parameterization: 75; 100; 150; 200; 250 %..
Cloud To Model Distance:
For the grid search evaluation the parameter set with the smallest qsm to cloud distance is chosen.
- For the cloud to model distance we choose [distance method] SECONDMOMENTUMORDERMSAC - minimize cropped (MSAC) root squared distance .
- For MSAC and inlier methods the distance is cropped at [crop distance] 0.030 (m)..
SphereFollowing Method Hyper Parameters:
First Hyper Parameters are set. Those parameters will never change during optimization.
- [SAC Model Consensus method] Center of Mass with median distance radius .
- The [inlier distance] for the selected SAC Model Consensus method is 0.030 (m). .
- For fitting a geometry with the selected SAC Model Consensus method at minimum [minPts]: 3 points are needed..
- For the selected SAC Model Consensus method [iterations]: 100 are performed..
- The radius of the search sphere during sphereFollowing is never smaller than [min global radius] 0.010 (m). .
- The algorithm is initialized on a close to ground slice with height [initialization height]: 0.05 (m)..
Données d'entrée
Structure des données d'entrée recherchées :
Result : Input Result QSM SphereFollowing
...
Cloud Group (Group)
Input Cloud QSM SphereFollowing (Item with points)
Result : Input Result QSM SphereFollowing
...
Cloud Group (Group)
Input Cloud QSM SphereFollowing (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.