Dijkstra Modeling
Plugin : SimpleForestNom de classe : SF_StepDijkstra
Description
Dijkstra Modeling - This implementation of the Dijkstra method utilizes an unsegmented tree cloud. On the modling parameters a downhill simplex search. is 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:
------------------------------------------------------------------------------
For this step please cite in addition the early method using Dijkstra for predicting tree skeleton:
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:
Dijkstra Method Hyper Parameters:
Hyper Parameters are set. Those parameters will never change during optimization.
Cloud To Model Distance:
For the downhill simplex search evaluation the parameter set with the smallest qsm to cloud distance is chosen.
- 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:
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] 0.020 (m). .
- Only the largest cluster will be processed with [clustering range] 0.10 (m). .
Dijkstra Method Hyper Parameters:
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]: 5 points are needed..
- For the selected SAC Model Consensus method [iterations]: 100 are performed..
Cloud To Model Distance:
For the downhill simplex 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 %..
- Test for each point its nearest [k] cylinders to get best nearest neighbor: 9 ..
- For MSAC and inlier methods the distance is cropped at [crop distance] 0.15 (m)..
- The [inlier distance]: 0.05 (m)..
Données d'entrée
Structure des données d'entrée recherchées :
Result : Input Result QSM Dijkstra
...
Cloud Group (Group)
Input Cloud QSM Dijkstra (Item with points)
Result : Input Result QSM Dijkstra
...
Cloud Group (Group)
Input Cloud QSM Dijkstra (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.
Xu H., Gossett N., Chen B.. 2007. Knowledge and heuristic-based modeling of laser-scanned trees.. ACM. Transactions on Graphics.
Rusu Radu Bogdan, Cousins Steve. 2011. 3d is here: Point cloud library (pcl). IEEE. Robotics and Automation (ICRA), 2011 IEEE International Conference on.
Xu H., Gossett N., Chen B.. 2007. Knowledge and heuristic-based modeling of laser-scanned trees.. ACM. Transactions on Graphics.
Rusu Radu Bogdan, Cousins Steve. 2011. 3d is here: Point cloud library (pcl). IEEE. Robotics and Automation (ICRA), 2011 IEEE International Conference on.