Computree Documentation

Dijkstra Modeling

Plugin : SimpleForest
Nom 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) :

  • 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)

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.