LIS Segmentation - prepare point cloud data for object based analysis and classification
This package contains a comprehensive set of point cloud segmentation and region growing tools, each dedicated to specialized tasks. Applications include the segmentation of point clouds by robust plane or line fitting and the segmentation of ground, building and vegetation points.
Tool: Block Segmentation
| Features |
|---|
| • point cloud segmentation by robust plane fitting |
| • data is processed in 2D or 3D tiles |
| • user specified number of planes to fit within each tile |
| • output attributes: region id, segment id, normal vector |
| Applications |
|---|
| • point cloud segmentation |
| • normal vector computation |
| • object based analysis/classification |
Tool: Clump Segmentation
| Features |
|---|
| • point cloud segmentation by robust plane fitting |
| • data is processed in clumps, identified by an attribute |
| • model points can be specified by a seed attribute |
| • output attributes: segment id, segment size, normal vector |
| Applications |
|---|
| • point cloud segmentation (esp. building roofs) |
| • normal vector computation |
| • object based analysis/classification |
Tool: Create Ground Seeds
| Features |
|---|
| • detection of ground seed points by iterative TIN densification |
| • constraining factors: normal tolerance, valid segment |
| • all valid segments that include a ground seed can be flagged as ground |
| Applications |
|---|
| • region growing |
| • point cloud classification |
Tool: Create Segment Seeds
| Features |
|---|
| • creation of seed points on point cloud segments based on attribute values |
| • seed points are determined by searching the minimum, maximum or median attribute value per segment |
| Applications |
|---|
| • region growing |
| • object based analysis/classification |
Tool: Density-based Spatial Clustering
| Features |
|---|
| • data clustering to group points that are closely packed together, marking points in low density regions as outliers (DBSCAN) |
| • clustering can be constrained by an attribute |
| • clusters can be filter based on size |
| • outputs: cluster id, cluster size |
| Applications |
|---|
| • data clustering |
| • region growing |
| • object based analysis/classification |
Tool: Hybrid Segmentation
| Features |
|---|
| • point cloud segmentation by distance-weighted cut and value aggregation |
| • input attributes: normal vector, 3D density, eigenvalues, planarity; optionally RGB |
| • outputs: plane offset, segment id |
| Applications |
|---|
| • point cloud segmentation |
| • object based analysis/classification |
Tool: Mean Shift Clustering
| Features |
|---|
| • point cloud segmentation by mean shift clustering on up to three point cloud attributes |
| • outputs: segment id, mean shifted point cloud (optional) |
| Applications |
|---|
| • point cloud segmentation |
| • object based analysis/classification |
| • point cloud smoothing |
Tool: Region Growing
| Features |
|---|
| • region growing based on attribute value similarity and nearest neighbor search |
| • up to three attributes, each with its own value tolerance, and/or normal vector similarity |
| • random seed or seed attribute |
| • 2D and 3D nearest neighbor search (knn/radius) |
| • outputs: segment id, segment size, segment class |
| Applications |
|---|
| • region growing |
| • object based analysis/classification |
Tool: Region Growing [interactive]
| Features |
|---|
| • interactive version of the Region Growing tool |
| • seed point is given by user (mouse-click) |
| Applications |
|---|
| • region growing |
| • object based analysis/classification |
Tool: Segmentation by Lines
| Features |
|---|
| • point cloud segmentation by robust line fitting |
| • threshold attribute constrains which points are processed |
| • 2D or 3D line fitting |
| • outputs: segment id, segment size, direction vector, slope, aspect |
| Applications |
|---|
| • point cloud segmentation |
| • direction vector computation |
| • object based analysis/classification |
Tool: Segmentation by Plane Growing
| Features |
|---|
| • point cloud segmentation by robust plane fitting and plane growing |
| • model points can be specified by a seed attribute, otherwise planarity is used |
| • radius search or k nearest neighbors |
| • output attributes: segment id, segment size, normal vector, slope, aspect, quality of fit |
| Applications |
|---|
| • point cloud segmentation |
| • normal vector computation |
| • object based analysis/classification |
Tool: Segmentation by Planes
| Features |
|---|
| • point cloud segmentation by robust plane fitting |
| • radius search or k nearest neighbors |
| • output attributes: segment id, segment size, normal vector, slope, aspect, quality of fit |
| Applications |
|---|
| • point cloud segmentation |
| • normal vector computation |
| • object based analysis/classification |
Tool: Stepwise Segmentation
| Features |
|---|
| • point cloud segmentation by robust plane fitting |
| • optimized for objects with well-defined edges |
| • model points can be specified by a seed attribute |
| • radius search |
| • output attributes: region id, segment id, segment size, normal vector |
| Applications |
|---|
| • point cloud segmentation (eps. buildings and building facades) |
| • normal vector computation |
| • object based analysis/classification |