CloudCompare wiki - User contributions [en]

VoxFall (plugin)

2025-04-02T15:50:55Z

Ioannis: /* Overview */

= Overview =

The VoxFall plugin is a simple and efficient way to detect and quantify rockfall events as discrete blocks directly between two 3D models.

[[File:VoxFall_product.jpg|center|frame]]

VoxFall does not rely on any distance computation between two models while is free from scale parameters associated to normals, spatial clustering, and cluster shape reconstruction.

The method treats the two input models as a single scene and applies two steps: 1) '''fitting an occupancy voxel grid''' of a resolution defined by the registration error; 2) '''empty space clustering''' and volume computation based on voxel adjacency.

[[File:VoxFall_grid.jpg|center]]

While VoxFall is a simple unified rockfall detection framework, it is recommended to read the original article by [https://doi.org/10.1016/j.enggeo.2025.108045 Farmakis et al. (2025)].

= Using VoxFall =

Select the two mesh models you want to compare and call: '''Plugins -> VOXFALL'''

[[File:VoxFall_dialog.JPG|center]]

== Model properties ==

The user has to define the ''voxel size'' and the orientation of the occupancy grid by means of the model's ''dip'' and ''dip direction''.

Since VoxFall is a non-parametric method and the results are only controlled by the quality of the input data, these two parameters are directly corresponding to input model properties.

*'''Voxel size:''' The registration error defines the ''voxel size'' (resolution), and each point in its distribution defines the probability of eliminating false positives (e.g., mean error=50%, max error=100%). A value corresponding to the LoD 95% is recommended.
*'''Dip:''' The slope model's dip angle in degrees [0-90]
*'''Dip direction:''' The slope model's dip direction (azimuth) in degrees [0-360]

The '''auto''' option computes the ''dip'' and ''dip direction'' parameters by fitting a plane to the input model.

== Output options ==

VoxFall by default exports a point cloud, representing the center of each voxel in the whole occupancy grid, with three scalar fields: ''Cluster ID'', ''Volume (m3)'' and ''Uncertainty (m3)'' of volume estimations for each detected cluster.

You can filter the grid by using the ''Cluster ID'' scalar field. VoxFall clusters are assigned IDs > 0, surface model is assigned -1, and the rest of the grid is 0.

You can choose to generate additional outputs:"
*'''Generate report (CSV):''' Generates a CSV inventory listing all the detected volumes with their attribute columns.
*'''Export meshes:''' Exports the mesh of the voxel assembly for each cluster.
*'''Loss/gain:''' A scalar field defining whether a cluster represents material loss or gain from Mesh #1 to Mesh #2. Loss = -1 and gain = 1, to ease multiplications with the ''volume (m3)'' scalar field.

VoxFall (plugin)

2025-03-26T09:11:16Z

Ioannis: /* Output options */

= Overview =

The VoxFall plugin is a simple and efficient way to detect and quantify rockfall events as discrete blocks directly between two 3D models.

[[File:VoxFall_product.jpg|center|frame]]

VoxFall does not rely on any distance computation between two models while is free from scale parameters associated to normals, spatial clustering, and cluster shape reconstruction.

The method treats the two input models as a single scene and applies two steps: 1) '''fitting an occupancy voxel grid''' of a resolution defined by the registration error; 2) '''empty space clustering''' and volume computation based on voxel adjacency.

[[File:VoxFall_grid.jpg|center]]

While VoxFall is a simple unified rockfall detection framework, it is recommended to read the original article by [https://iaeg.info/wp-content/uploads/2024/10/03.-Emerging-Technologies-and-Applications-in-Engineering-Geology-and-Geotechnics.pdf Farmakis et al. (2025)].

= Using VoxFall =

Select the two mesh models you want to compare and call: '''Plugins -> VOXFALL'''

[[File:VoxFall_dialog.JPG|center]]

== Model properties ==

The user has to define the ''voxel size'' and the orientation of the occupancy grid by means of the model's ''dip'' and ''dip direction''.

Since VoxFall is a non-parametric method and the results are only controlled by the quality of the input data, these two parameters are directly corresponding to input model properties.

*'''Voxel size:''' The registration error defines the ''voxel size'' (resolution), and each point in its distribution defines the probability of eliminating false positives (e.g., mean error=50%, max error=100%). A value corresponding to the LoD 95% is recommended.
*'''Dip:''' The slope model's dip angle in degrees [0-90]
*'''Dip direction:''' The slope model's dip direction (azimuth) in degrees [0-360]

The '''auto''' option computes the ''dip'' and ''dip direction'' parameters by fitting a plane to the input model.

== Output options ==

VoxFall by default exports a point cloud, representing the center of each voxel in the whole occupancy grid, with three scalar fields: ''Cluster ID'', ''Volume (m3)'' and ''Uncertainty (m3)'' of volume estimations for each detected cluster.

You can filter the grid by using the ''Cluster ID'' scalar field. VoxFall clusters are assigned IDs > 0, surface model is assigned -1, and the rest of the grid is 0.

You can choose to generate additional outputs:"
*'''Generate report (CSV):''' Generates a CSV inventory listing all the detected volumes with their attribute columns.
*'''Export meshes:''' Exports the mesh of the voxel assembly for each cluster.
*'''Loss/gain:''' A scalar field defining whether a cluster represents material loss or gain from Mesh #1 to Mesh #2. Loss = -1 and gain = 1, to ease multiplications with the ''volume (m3)'' scalar field.

VoxFall (plugin)

2025-03-26T09:05:58Z

Ioannis: /* Model properties */

= Overview =

The VoxFall plugin is a simple and efficient way to detect and quantify rockfall events as discrete blocks directly between two 3D models.

[[File:VoxFall_product.jpg|center|frame]]

VoxFall does not rely on any distance computation between two models while is free from scale parameters associated to normals, spatial clustering, and cluster shape reconstruction.

The method treats the two input models as a single scene and applies two steps: 1) '''fitting an occupancy voxel grid''' of a resolution defined by the registration error; 2) '''empty space clustering''' and volume computation based on voxel adjacency.

[[File:VoxFall_grid.jpg|center]]

While VoxFall is a simple unified rockfall detection framework, it is recommended to read the original article by [https://iaeg.info/wp-content/uploads/2024/10/03.-Emerging-Technologies-and-Applications-in-Engineering-Geology-and-Geotechnics.pdf Farmakis et al. (2025)].

= Using VoxFall =

Select the two mesh models you want to compare and call: '''Plugins -> VOXFALL'''

[[File:VoxFall_dialog.JPG|center]]

== Model properties ==

The user has to define the ''voxel size'' and the orientation of the occupancy grid by means of the model's ''dip'' and ''dip direction''.

Since VoxFall is a non-parametric method and the results are only controlled by the quality of the input data, these two parameters are directly corresponding to input model properties.

*'''Voxel size:''' The registration error defines the ''voxel size'' (resolution), and each point in its distribution defines the probability of eliminating false positives (e.g., mean error=50%, max error=100%). A value corresponding to the LoD 95% is recommended.
*'''Dip:''' The slope model's dip angle in degrees [0-90]
*'''Dip direction:''' The slope model's dip direction (azimuth) in degrees [0-360]

The '''auto''' option computes the ''dip'' and ''dip direction'' parameters by fitting a plane to the input model.

== Output options ==

VoxFall by default exports a point cloud, representing the center of each voxel in the whole occupancy grid, with three scalar fields: ''Cluster ID'', ''Volume (m3)'' and ''Uncertainty (m3)'' of volume estimations for each detected cluster.

You can filter the grid by using the ''Cluster ID'' scalar field. VoxFall clusters are assigned IDs > 0, surface model is assigned -1, and the rest of the grid is 0.

You can choose to generate additional outputs:"
*'''Export meshes:''' Exports the mesh of the voxel assembly for each cluster.
*'''Loss/gain:''' A scalar field defining whether a cluster represents material loss or gain from Mesh #1 to Mesh #2. Loss = -1 and gain = 1, to ease multiplications with the ''volume (m3)'' scalar field.

Plugins

2025-03-26T09:00:40Z

Ioannis: /* Standard plugins */

= Standard plugins =

* [Windows, macOS, Linux] [[Compass (plugin) | Compass]] Easy digitization of geological structures and structural traces on point clouds
* [Windows, macOS] [[Virtual broom (plugin) | Virtual Broom]] Efficient (road) scan cleaning
* [Windows, macOS] [[HoughNormals (plugin) | Hough Normals]] Normals computation
* [Windows, macOS] [[Hidden Point Removal (plugin) | HPR]] Hidden Points Removal
* [Windows, Linux] [[Point Cloud Library Wrapper (plugin) | PCL]] Interface for [http://pointclouds.org/ PCL] library (normals computation, outliers removal, etc.)
* [Windows, macOS] [[ShadeVis (plugin) | PCV]] Ambient Occlusion for mesh or point cloud (output: per-point 'visibility' as a scalar field)
* [Windows, macOS, Linux] [[Poisson Surface Reconstruction (plugin) | Poisson Surface Reconstruction]] Surface Mesh Reconstruction (for closed surfaces)
* [Windows, Linux] [[RANSAC Shape Detection (plugin) | Ransac Shape Detection]] Automatic RANSAC Shape Detection
* [Windows, macOS] [[Surface of Revolution Analysis (plugin) | SRA]] Surface of Revolution Analysis (comparison between a point cloud and a surface of revolution)
* [Windows] [[CANUPO (plugin) | CANUPO]] Point Cloud Classification with CANUPO
* [Windows] [https://lidar.univ-rennes.fr/en/3dmasc 3DMASC] Advanced Point Cloud Classification with Multiple Attributes, Scales and Clouds
* [Windows, macOS, Linux] [[M3C2 (plugin) | M3C2]] Computation of robust signed distances between point clouds
* [Windows] [[Cork (plugin) | Cork]] Mesh Boolean Operations (Constructive Solid Geometry) based on the [https://github.com/cloudcompare/cork Cork] library
* [Windows] [[Mesh Boolean (plugin) | Mesh Boolean]] Mesh Boolean Operations (Constructive Solid Geometry) based on the [https://libigl.github.io/ libigl] library
* [Windows, macOS] [[Animation (plugin) | Animation]] Animation rendering plugin
* [Windows, macOS] [[Facets (plugin) | Facets]] Structural geology plugin
* [Windows, macOS] [[CSF (plugin) | CSF]] Automatic ground/non-ground classification algorithm based on Cloth Simulation
* [Windows] [[MPlane (plugin) | MPlane]] Normal distance measurements against a defined plane.
* [Windows] [[Colorimetric Segmenter (plugin) | Colorimetric Segmenter]] Color-based segmentation of point clouds.
* [Windows] [[Masonry Segmentation (plugin) | Masonry Segmentation]] segmentation of dense point clouds of masonry structures into their individual stones.
* [Windows, macOS] [[qCloudLayers (plugin) | Cloud Layers]] Manual classification of a cloud
* [Windows, ?] [[ treeiso (plugin) | TreeIso]] Individual-tree isolation (treeiso) from terrestrial laser scanning
* [Windows, linux] [[ 3DFin (plugin) | 3DFin]] 3D Forest Inventory
* [Windows, Linux] [[ VoxFall (plugin) | VoxFall]] Non-Parametric Volumetric Change Detection for Rockfalls

= Deprecated =
* [[Kinect (plugin) | qKinect]] Acquisition of colored point clouds with a Kinect device

= OpenGL 'shaders' plugins =

* [[Blur (shader) | qBlur]] (Simple screen blurring - for demo)
* [[EDL (shader) | qEDL]] (Eye Dome Lighting)
* [[SSAO (shader) | qSSAO]] (Screen Space Ambient Occlusion)

File:VoxFall dialog.JPG

2025-03-26T08:51:35Z

Ioannis: Ioannis reverted File:VoxFall dialog.JPG to an old version

File:VoxFall dialog.JPG

2025-03-26T08:51:00Z

Ioannis: Ioannis reverted File:VoxFall dialog.JPG to an old version

File:VoxFall dialog.JPG

2025-03-26T08:35:09Z

Ioannis: Ioannis uploaded a new version of File:VoxFall dialog.JPG

VoxFall (plugin)

File:VoxFall framework.jpg

2025-03-08T15:04:49Z

Ioannis:

Plugins

2025-03-08T13:18:49Z

Ioannis: /* Standard plugins */

= Standard plugins =

* [Windows, macOS, Linux] [[Compass (plugin) | Compass]] Easy digitization of geological structures and structural traces on point clouds
* [Windows, macOS] [[Virtual broom (plugin) | Virtual Broom]] Efficient (road) scan cleaning
* [Windows, macOS] [[HoughNormals (plugin) | Hough Normals]] Normals computation
* [Windows, macOS] [[Hidden Point Removal (plugin) | HPR]] Hidden Points Removal
* [Windows, Linux] [[Point Cloud Library Wrapper (plugin) | PCL]] Interface for [http://pointclouds.org/ PCL] library (normals computation, outliers removal, etc.)
* [Windows, macOS] [[ShadeVis (plugin) | PCV]] Ambient Occlusion for mesh or point cloud (output: per-point 'visibility' as a scalar field)
* [Windows, macOS, Linux] [[Poisson Surface Reconstruction (plugin) | Poisson Surface Reconstruction]] Surface Mesh Reconstruction (for closed surfaces)
* [Windows, Linux] [[RANSAC Shape Detection (plugin) | Ransac Shape Detection]] Automatic RANSAC Shape Detection
* [Windows, macOS] [[Surface of Revolution Analysis (plugin) | SRA]] Surface of Revolution Analysis (comparison between a point cloud and a surface of revolution)
* [Windows] [[CANUPO (plugin) | CANUPO]] Point Cloud Classification with CANUPO
* [Windows] [https://lidar.univ-rennes.fr/en/3dmasc 3DMASC] Advanced Point Cloud Classification with Multiple Attributes, Scales and Clouds
* [Windows, macOS, Linux] [[M3C2 (plugin) | M3C2]] Computation of robust signed distances between point clouds
* [Windows] [[Cork (plugin) | Cork]] Mesh Boolean Operations (Constructive Solid Geometry) based on the [https://github.com/cloudcompare/cork Cork] library
* [Windows] [[Mesh Boolean (plugin) | Mesh Boolean]] Mesh Boolean Operations (Constructive Solid Geometry) based on the [https://libigl.github.io/ libigl] library
* [Windows, macOS] [[Animation (plugin) | Animation]] Animation rendering plugin
* [Windows, macOS] [[Facets (plugin) | Facets]] Structural geology plugin
* [Windows, macOS] [[CSF (plugin) | CSF]] Automatic ground/non-ground classification algorithm based on Cloth Simulation
* [Windows] [[MPlane (plugin) | MPlane]] Normal distance measurements against a defined plane.
* [Windows] [[Colorimetric Segmenter (plugin) | Colorimetric Segmenter]] Color-based segmentation of point clouds.
* [Windows] [[Masonry Segmentation (plugin) | Masonry Segmentation]] segmentation of dense point clouds of masonry structures into their individual stones.
* [Windows, macOS] [[qCloudLayers (plugin) | Cloud Layers]] Manual classification of a cloud
* [Windows, ?] [[ treeiso (plugin) | TreeIso]] Individual-tree isolation (treeiso) from terrestrial laser scanning
* [Windows, linux] [[ 3DFin (plugin) | 3DFin]] 3D Forest Inventory
* [Windows, linux] [[ VoxFall (plugin) | VoxFall]] Non-Parametric Volumetric Change Detection for Rockfalls

= Deprecated =
* [[Kinect (plugin) | qKinect]] Acquisition of colored point clouds with a Kinect device

= OpenGL 'shaders' plugins =

* [[Blur (shader) | qBlur]] (Simple screen blurring - for demo)
* [[EDL (shader) | qEDL]] (Eye Dome Lighting)
* [[SSAO (shader) | qSSAO]] (Screen Space Ambient Occlusion)