Point Cloud Context Menu
Commands available when right-clicking a Point Cloud object in the Data Tree. Point clouds represent collections of 3D points with optional attributes (colour, intensity, normal vectors, etc.) typically acquired from laser scanners, photogrammetry, or other capture methods.
Navigation & Display
Navigate To
Menu name: Navigate To
Tooltip Navigate to the item in the 3D view.
What it does Centres and zooms the 3D viewport camera to frame the selected point cloud. The camera position is calculated based on the point cloud's bounding box and current view orientation, ensuring the entire cloud is visible. Does not modify the point cloud data itself.
When to use it
- You've selected a point cloud in the project tree but can't see it in the 3D view
- You want to quickly focus on a specific scan after navigating a large scene
- You've lost your position and need to return to a known dataset
Notes If the point cloud is not currently loaded into memory, it will be automatically loaded first. The camera maintains its current viewing angle (pitch/yaw) unless maintaining the camera angle would result in a poor view.
Set As Active
Menu name: Set As Active
Tooltip Set this point cloud as the active object for operations.
What it does Designates the selected point cloud as the "active" point cloud in the project. Many operations reference the active point cloud as a target (e.g., attaching polylines, filtering other clouds, creating DEMs). The active cloud is highlighted in the project tree with a distinct icon.
When to use it
- Before attaching structural measurements or polylines to a specific surface
- When using IDW or other interpolation operations that require a target surface
- When performing classification or filtering operations that reference a base cloud
Notes Only one point cloud can be active at a time. Setting a new active cloud replaces the previous one. The active state persists across sessions and is saved in the project database.
Look Through
Menu name: Look Through
Tooltip Position the camera at the scanner origin looking outward.
What it does Moves the 3D viewport camera to the scanner origin position (if defined) and orients the view direction to match the scanner's original perspective. This recreates the view from the scanner location when the data was captured. If no scanner origin is set, uses the geometric centre of the point cloud.
When to use it
- Reviewing data from the scanner's original perspective
- Understanding occlusions and shadowing in the captured data
- Planning additional scan positions to fill gaps
- Quality checking registration by viewing from scan positions
Notes
Works best when scanner origin has been set (either from import metadata or manually using "Set Scanner Origin"). Without a defined origin, the view may not accurately represent the capture perspective.
Selection
By RGB Color
Menu name: By RGB Color
Tooltip Select points based on RGB colour values.
What it does Opens a dialogue to define RGB colour ranges for selection. Points with colour values within the specified ranges are selected (highlighted in the 3D view). Selection criteria include RGB thresholds and optional tolerance values. Multiple selection operations can be combined using "add to selection" or "remove from selection" modes.
When to use it
- Isolating specific features based on colour (e.g., vegetation, rock types, man-made objects)
- Selecting points from photographs or texture-mapped data
- Pre-filtering before classification or extraction operations
- Quality control to identify and remove coloured artifacts
Notes Requires point cloud to have RGB colour attributes. Selection is based on 8-bit RGB values (0-255 per channel). Large point clouds may take time to process the selection criteria.
Inside Visible Polylines
Menu name: Inside Visible Polylines
Tooltip Select points inside all visible polylines.
What it does Selects all points that fall inside the boundaries of currently visible 2D or 3D polylines. For 2D polylines, projects points onto the polyline plane and performs 2D containment testing. For 3D polylines, creates a prismatic or extruded volume. Can use multiple polylines simultaneously - points inside any polyline are selected.
When to use it
- Extracting specific areas of interest defined by interpreted boundaries
- Isolating geological features traced with polylines
- Selecting regions for detailed analysis or separate export
- Creating masks for attribute calculations
Notes Only polylines with visibility enabled are used. Polylines must form closed loops for interior selection. Selection performance depends on polyline complexity and point cloud size. For large datasets, consider using octree-based splitting first.
Deselect All
Menu name: Deselect All
Tooltip Clear all point selections.
What it does Removes selection state from all points in the point cloud, returning them to normal display state. Selected points lose their highlight colour and return to their original rendering style. This is a quick operation that does not modify point data.
When to use it
- Clearing selection before starting a new selection operation
- Returning to normal view after completing selection-based operations
- Canceling accidental or incorrect selections
Notes None.
Filtering & Cleanup
Decluster
Menu name: Decluster
Tooltip Reduce point density using grid-based decluttering.
What it does Opens a dialogue to specify cell size (in metres) for grid-based decimation. Divides the point cloud into a 3D grid of cubic cells. Within each cell, keeps only the point closest to the cell centre, removing all others. This produces a more uniformly spaced point cloud with reduced overall density. The operation is performed as a background process.
When to use it
- Reducing point density for faster visualization and processing
- Creating more uniform point spacing from variable-density scans
- Pre-processing before meshing operations that benefit from uniform spacing
- Removing clustering artifacts from multi-station scans with overlapping coverage
Notes
This operation permanently removes points. Consider duplicating the point cloud first or adjusting the cell size conservatively. Smaller cell sizes preserve more points but provide less decluttering.
Points are marked as filtered/deleted but can be restored using "Restore Deleted" before saving. Typical cell sizes range from 0.05m (high density) to 0.5m (aggressive reduction).
Remove Duplicates
Menu name: Remove Duplicates
Tooltip Remove points with identical XYZ coordinates.
What it does Scans the point cloud for points that share identical X, Y, and Z coordinates (within floating-point precision). When duplicates are found, keeps only one instance and marks the others as deleted. This is a spatial uniqueness operation that does not consider point attributes like colour or intensity.
When to use it
- Cleaning up data after merging multiple overlapping scans
- Removing artifacts from registration or alignment operations
- Reducing file size when duplicate geometry exists from processing
- Preparing data for meshing operations that require unique vertices
Notes Comparison uses standard floating-point precision (~1e-6 metres for typical coordinates). Points very close together but not exactly identical are not removed - use Decluster for proximity-based reduction. Operation can be slow on very large point clouds (>50M points).
Reduce Overlap
Menu name: Reduce Overlap
Tooltip Reduce point density in overlapping scan regions.
What it does Analyzes multiple selected point clouds to identify overlapping regions where multiple scans cover the same area. In overlap zones, intelligently reduces point density to maintain coverage while eliminating redundancy. The algorithm considers factors like scan angle, distance from scanner origin, and point quality. Works on multiple point clouds simultaneously.
When to use it
- Optimizing multi-scan projects with significant overlap between stations
- Reducing file size and point count while maintaining coverage
- Improving visualization performance by removing redundant overlap points
- Preparing datasets for photogrammetry or texture mapping
Notes
Requires multiple point clouds to be selected simultaneously. Point clouds must be loaded into memory. Works best when scanner origins are defined for each cloud.
This operation considers scan geometry - points near the scanner origin or at good incidence angles are preferred over distant or oblique points. Can significantly reduce project size for high-overlap projects (6+ scans per area).
Clip By Range
Menu name: Clip By Range
Tooltip Remove points beyond a specified distance from scanner origin.
What it does Opens a dialogue to specify minimum and maximum range values. Points are classified by their distance from the scanner origin (if defined) or project origin. Points outside the specified range are marked as filtered/deleted. This creates a spherical or annular selection zone around the origin point.
When to use it
- Removing distant, low-quality points from scan edges
- Isolating a specific distance band for analysis
- Removing near-range noise or scanner mounting artifacts
- Creating range-normalized datasets for comparison
Notes Requires scanner origin to be set for meaningful results. If no scanner origin is defined, uses the project origin (0,0,0) which may not be useful. Range is calculated as Euclidean distance in 3D space. Typical values range from 0.5m (minimum) to scanner maximum range (often 50-300m depending on scanner).
Transform
Scale
Menu name: Scale
Tooltip Scale the point cloud by specified factors.
What it does Opens the Transform dialogue with scale mode enabled. Allows specification of separate scale factors for X, Y, and Z axes. All point coordinates are multiplied by the respective factors, resizing the point cloud. Uniform scaling (same factor for all axes) preserves shape; non-uniform scaling can stretch or compress along specific axes. Scanner origin (if defined) is used as the scale centre.
When to use it
- Converting between unit systems (metres to feet: use factor 3.28084)
- Correcting scale errors from import or registration
- Resizing objects to match reference data or drawings
- Adjusting vertical exaggeration for visualization
Notes See Scale in Shared Commands for general usage. For point clouds specifically: scanner origin is preserved and used as scale centre. Attributes like "Range" become invalid after non-uniform scaling and should be recalculated. Scale factors multiply coordinates (2.0 = double size, 0.5 = half size).
Rotate
Menu name: Rotate
Tooltip Rotate the point cloud around specified axes.
What it does Opens the Rotation dialogue to specify rotation angles (in degrees) around X, Y, and Z axes. Rotations are applied sequentially (typically Z, then Y, then X) around the scanner origin or geometric centre. The Z-axis rotation is most commonly used for azimuthal alignment (orienting to geographic north).
When to use it
- Aligning scan data to geographic north or project coordinate system
- Correcting scanner mounting angles or tilt
- Orienting data for optimal visualization or analysis
- Matching rotation between multiple scan stations
Notes See Rotate in Shared Commands for general usage. For point clouds: rotation centre is scanner origin if defined, otherwise geometric centroid. Scanner origin position is also rotated. Normal vectors (if present) are automatically updated to match rotation. Order of rotation matters - Z-Y-X sequence is standard but produces different results than other orders.
Remove Dip
Menu name: Remove Dip
Tooltip Rotate point cloud to remove regional dip/tilt.
What it does Opens a dialogue to define the regional dip direction (azimuth) and dip angle. Calculates and applies a rotation that removes the specified dip, effectively "leveling" bedding planes or tilted surfaces. This is a specialized rotation operation common in structural geology workflows, particularly for outcrop models where bedding is not horizontal.
When to use it
- Restoring structural measurements to pre-deformation orientation (palinspastic restoration)
- Leveling tilted outcrop models for analysis
- Removing regional dip before measuring fold geometries
- Creating horizontal datum for sedimentary analysis
Notes Dip direction follows geological convention (azimuth of down-dip direction, 0-360°). Dip angle is measured from horizontal (0° = horizontal, 90° = vertical). The operation calculates the inverse rotation needed to return beds to horizontal. Scanner origin is maintained as rotation centre.
Attributes & Analysis
Tensor Analysis
Menu name: Tensor Analysis
Tooltip Calculate fabric tensor from local point neighborhoods.
What it does Opens the Tensor Analysis dialogue to specify neighborhood radius and options. For each point, analyses the local geometric fabric within the specified radius by computing eigenvalues and eigenvectors of the covariance matrix of neighboring points. Creates three new attributes: linearity (λ1-λ2), planarity (λ2-λ3), and sphericity (λ3), representing local geometric fabric. Optionally calculates curvature and normal orientation. Runs as a background process.
When to use it
- Identifying planar surfaces (bedding, fractures, faults) via high planarity values
- Detecting linear features (edges, ridges) via high linearity values
- Extracting structural orientation from unorganized point clouds
- Pre-processing for classification or segmentation operations
- Quality assessment of surface geometry
Notes
Computationally intensive - can be very slow for large point clouds (>10M points). Consider using on decimated data first. Radius selection is critical: too small captures noise, too large smooths important features. Typical radius: 0.1-0.5m for outcrop scans.
Requires point cloud to be loaded in memory. Results are stored as three separate attribute channels that can be visualised with false colour or used for filtering.
Range
Menu name: Range
Tooltip Calculate distance from scanner origin for each point.
What it does Computes the Euclidean distance from each point to the scanner origin (if defined) or project origin. Stores the result as a new attribute channel named "Range". This attribute represents the original acquisition distance for each point, useful for quality analysis and range-dependent filtering.
When to use it
- Visualizing data quality degradation with distance
- Creating range-based false colour maps
- Filtering points by acquisition distance
- Quality control for scanner performance analysis
- Identifying range-dependent noise or errors
Notes
Most meaningful when scanner origin is defined (from scan metadata or manually set). If no scanner origin exists, calculates distance from project origin (0,0,0) which may not be useful.
Range is calculated as: √(x² + y² + z²) relative to scanner position. Fast operation that completes immediately even on large point clouds. Range values are in project units (typically metres).
Nearest Neighbor Distance
Menu name: Nearest Neighbor Distance
Tooltip Calculate distance to closest neighboring point.
What it does For each point, finds the single nearest neighboring point and calculates the 3D Euclidean distance between them. Stores the result as a new attribute channel. This provides a measure of local point density - smaller values indicate denser spacing, larger values indicate sparse regions or isolated points. Uses efficient spatial indexing for fast computation.
When to use it
- Identifying regions of varying point density
- Detecting isolated noise points (unusually large distances)
- Assessing scan quality and coverage uniformity
- Pre-processing for decluttering - visualise before choosing cell size
- Quality control to find gaps or holes in coverage
Notes Fast operation using octree spatial indexing. Results are in project units (typically metres). Very small values (less than 0.001m) may indicate duplicate or near-duplicate points. Very large values may indicate isolated noise that should be removed. Use false colour visualization to identify patterns.
Object Operations
Duplicate
Menu name: Duplicate
Tooltip Create an independent copy of this point cloud.
What it does Creates a complete independent copy of the selected point cloud in the project database. The duplicate includes all points, attributes, display properties, scanner origin, and metadata. The copy is assigned a new name (e.g., "Scan001_Copy") and appears as a new entry in the project tree. Both clouds can be modified independently.
When to use it
- Testing destructive operations (decimation, filtering) without risking original data
- Creating multiple processing variants with different parameters
- Preserving original data before applying irreversible transformations
- Generating separate copies for different analysis workflows
Notes
Duplicates consume significant disk space - copying a 50M point cloud requires ~1-2GB additional storage depending on attributes. Ensure sufficient disk space before duplicating large clouds.
Duplication is immediate (creates database entry and new folder) but full data copy may occur in background. The duplicate is fully independent - deleting one does not affect the other.
Delete
Menu name: Delete
Tooltip Permanently remove this point cloud from the project.
What it does Permanently removes the selected point cloud from the project database and deletes all associated files from disk. The point cloud is removed from the project tree, all attributes are deleted, and storage space is freed. This operation cannot be undone.
When to use it
- Removing erroneous or unwanted data from the project
- Cleaning up test or intermediate processing results
- Freeing disk space by removing large unused clouds
- Simplifying project structure
Notes
This operation is permanent and cannot be undone. Ensure you have backups or have exported the data before deleting. All dependent objects (attributes, linked measurements) are also deleted.
If other objects reference this point cloud (e.g., as the active cloud for operations), those references become invalid. Consider exporting valuable data before deletion.
Optimization
Load Into Memory
Menu name: Load Into Memory
Tooltip Load point cloud data into RAM for faster operations.
What it does Loads the complete point cloud dataset from disk into system RAM. While loaded, all operations (selection, filtering, attribute calculation, rendering) are significantly faster. The point cloud remains in memory until explicitly unloaded or the project is closed. Memory usage equals approximately 12-20 bytes per point depending on attributes.
When to use it
- Before performing multiple operations on a large point cloud
- When working interactively and speed is important
- Prior to operations that require full dataset access (meshing, tensor analysis)
- When system RAM is sufficient to hold the dataset
Notes
Loading large point clouds can consume significant RAM. A 10M point cloud requires ~200-300MB, a 50M point cloud ~1-1.5GB. Ensure sufficient free RAM before loading. Loading multiple large clouds simultaneously can exhaust system memory.
Point clouds can be loaded on-demand automatically, but explicit loading provides better performance. Use "Unload From Memory" to free RAM when done.
Unload From Memory
Menu name: Unload From Memory
Tooltip Drop the point cloud from memory but keep it in the project explorer.
What it does Removes the point cloud data from system RAM, freeing the memory for other operations. The point cloud remains in the project tree and database - only the in-memory copy is discarded. Data can be reloaded from disk when needed. Display, metadata, and tree structure are preserved.
When to use it
- Freeing RAM after completing operations on large point clouds
- Managing memory when working with multiple large datasets
- When switching focus to other project areas
- Before loading other large objects into memory
Notes Does not delete the point cloud or its data files - only unloads from RAM. The point cloud will be automatically reloaded when needed for operations or rendering, but with slower performance. Unloading is immediate and does not save changes (use "Mark As Modified" and save project to persist changes).