Segmentation#
The Segmentation tab provides tools for refinement, clustering and analysis.
Merge#
Combines multiple clusters or creates new clusters from point selections:
For complete clusters:
Select multiple clusters in the Object Browser
Click Merge in the ribbon or press
m
after clicking the viewport.Selected clusters are combined into a single new cluster
For point selections:
Use area selection (
R
key) to select points from one or more clustersClick Merge or press
m
after clicking the viewport.A new cluster is created containing only the selected points
Original clusters remain but without the selected points
Remove#
Deletes selected clusters or removes points from clusters:
For complete clusters:
Select one or more clusters in the Object Browser
Click Remove or press
Delete
after clicking the viewport.Selected clusters are completely deleted
For point selections:
Use area selection (
R
key) to select points within clustersClick Remove or press
Delete
after clicking the viewport.Only the selected points are removed from their parent clusters
Empty clusters are automatically deleted
Select by Size#
Filters clusters by point count:
Click Select in the ribbon
Adjust the slider to set a minimum size threshold
Clusters below the threshold are automatically selected
Use in combination with Remove to clean up small clusters
Transform#
Applies rotation and translation to clusters:
Select a cluster in the Object Browser
Click Transform
A 3D transformation widget appears around the cluster
Use the transformation widget to move or rotate the cluster
Press Transform again to exit transformation mode
Crop#
Trims points based on distance to other structures:
Click Crop
Select source structures to crop
Select target structures to measure distance from
Set the distance threshold
Choose to keep points within or beyond the threshold
Cluster#
Groups points into separate clusters:
Select a cluster with multiple distinct structures
Click Cluster
Choose clustering method:
Connected Components: Groups connected components (default). Particularly useful for postprocessing volume segmentations.
DBSCAN: Density-based clustering with distance and minimum points parameters
K-Means: Divides into a specified number of clusters
Birch: Hierarchical clustering using Clustering Feature Trees, ideal for large datasets
Configure method-specific parameters:
- DBSCAN:
Distance: Maximum distance between points in the same cluster
Min Points: Minimum points required to form a cluster
- K-Means:
K: Number of target clusters
- Birch:
Clusters: Number of target clusters
Threshold: Radius threshold for merging subclusters (lower values create more clusters)
Branching Factor: Maximum subclusters per node (affects memory usage and clustering speed)
Click OK to apply clustering
Outlier Removal#
Removes noise points using statistical methods:
Select a cluster to clean
Click Outlier
Choose removal method:
Statistical: Removes points based on distance to neighbors
Eigenvalue: Removes edge points using covariance analysis
Configure parameters:
Neighbors: Number of neighbors to consider for statistics
Threshold: Sensitivity of outlier detection (lower = more aggressive)
Trim#
Select points outside specified axis-aligned boundaries:
Select a cluster
Click Trim
Two cutting planes appear in the 3D viewer
Position the planes by dragging or use keyboard shortcuts:
X
: Align planes to X-axisY
: Align planes to Y-axisZ
: Align planes to Z-axis
Points between the planes are preserved
Press Trim again to exit trim mode
Thin#
Reduces point density while preserving structure:
Select a cluster
Click Thin
Choose thinning method:
Outer: Keep surface/hull points
Core: Keep central/medoid points
Inner: Keep interior points using ray-casting
Click OK to apply thinning
Downsample#
Reduces the number of points while maintaining overall structure:
Select a cluster
Click Downsample
Choose downsampling method:
Radius: Remove points within a specified distance of each other
Number: Randomly subsample to a target number of points
Configure parameters:
Radius: Minimum distance between retained points
Size: Target number of points for random subsampling
Click OK to apply downsampling
Distances#
Analyzes distance distributions between clusters:
Click Distances
In the dialog:
Select source clusters/models to measure from
Select target clusters/models to measure to
Configure distance calculation parameters
View results:
Distance histograms and statistics
Minimum, maximum, mean, and standard deviation
Export data as CSV for external analysis
Use results to inform clustering or filtering decisions
Properties#
Advanced analysis and visualization dialog with three modes: Visualize, Distribution, and Statistics.
Select objects in the Object Browser
Click Properties in the ribbon
Use Compute to calculate properties, then switch between tabs
Property Categories:
Distance: To camera, clusters, or models
Surface: Curvature, edge length, surface area, volume
Geometric: Dimensions, point counts, identity
Projection: Projected curvature, geodesic distance
Visualization Options:
Color Maps: Common colormaps (viridis, plasma, etc.)
Normalization: Per-object or global scaling
Quantiles: Statistical binning for outlier handling
Interactive: Real-time color mapping in 3D viewport
Visualize Tab: Compute geometric properties and display as interactive color maps in the 3D viewport.
Distribution Tab: Generate interactive export-ready histograms, density plots, and line charts with customizable styling.
Statistics Tab: View numerical summaries (min, max, mean, std dev) and export data as CSV/TSV files.
Tip
All data can be exported using the Export Data button.