The 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 |
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• 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 |
Application |
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• point cloud segmentation |
• normal vector computation |
• object based analysis/classification |
Tool: Clump Segmentation
Features |
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• 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 |
Application |
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• point cloud segmentation (esp. building roofs) |
• normal vector computation |
• object based analysis/classification |
Tool: Create Ground Seeds
Features |
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• 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 |
Application |
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• region growing |
• point cloud classification |
Tool: Create Segment Seeds
Features |
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• 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 |
Application |
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• region growing |
• object based analysis/classification |
Tool: Density-based Spatial Clustering
Features |
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• 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 |
Application |
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• data clustering |
• region growing |
• object based analysis/classification |
Tool: Dijkstra Growing
Features |
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• point cloud segmentation based on Dijkstra region growing from seed points |
• iterative region growing to handle data gaps |
• outputs: segment id, parent id, path distance, iteration, distance difference |
Application |
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• point cloud segmentation (esp. trees) |
• object based analysis/classification |
Tool: Hybrid Segmentation
Features |
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• 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 |
Application |
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• point cloud segmentation |
• object based analysis/classification |
Tool: Invert Normals
Features |
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• inversion of point cloud normals |
Application |
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• inverting normal direction |
Tool: Orient Normals
Features |
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• re-orient normals of a point cloud in a consistent way |
• methods: user-defined reference normal, global normal, normal per cluster, direction to scanner, minimum spanning tree |
Application |
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• correction of normal vectors |
Tool: Region Growing
Features |
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• 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 |
Application |
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• region growing |
• object based analysis/classification |
Tool: Region Growing [interactive]
Features |
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• interactive version of the Region Growing tool |
• seed point is given by user (mouse-click) |
Application |
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• region growing |
• object based analysis/classification |
Tool: Segment Centroids
Features |
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• condense segments to their centroids |
• optional: output of mean attribute value per segment |
Application |
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• point cloud segmentation |
• object based analysis/classification |
Tool: Segment Thinning
Features |
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• thinning of points on point cloud segments by 3D block filtering |
• filter methods: lowest, highest, nearest, mean, median, z-slice mean, min(attribute), max(attribute), center |
Application |
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• point cloud thinning |
Tool: Segmentation by Lines
Features |
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• 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 |
Application |
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• point cloud segmentation |
• direction vector computation |
• object based analysis/classification |
Tool: Segmentation by Plane Growing
Features |
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• 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 |
Application |
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• point cloud segmentation |
• normal vector computation |
• object based analysis/classification |
Tool: Segmentation by Planes
Features |
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• 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 |
Application |
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• point cloud segmentation |
• normal vector computation |
• object based analysis/classification |
Tool: Stepwise Segmentation
Features |
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• 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 |
Application |
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• point cloud segmentation (eps. buildings and building facades) |
• normal vector computation |
• object based analysis/classification |