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A point cloud is a collection of three-dimensional measurement points that precisely describe the surfaces of an object or building in space. It is usually created through laser scanning (LiDAR) or photogrammetry and forms the basis for plans, models and analyses.
Why is a point cloud important?
- Precision and completeness: Millions of points with millimeter-to-centimeter accuracy reduce rework and repeat appointments.
- Versatile derivation: Point clouds can be used to create floor plans, sections, elevations, orthoprojections and BIM models.
- Objectivity and documentation: QA protocols such as check points, RMS values and tolerances create trust, auditability and legal confidence.
- Seamless integration: Direct data flow into CAD/BIM formats such as DWG, DXF and IFC, as well as CAFM and analysis workflows.
How is a point cloud created?
- Laser scanning: In laser scanning, laser pulses are emitted and their reflections are measured. Each measured point receives an exact position in space. Modern scanners capture several hundred thousand to millions of measurement points per second, helping to create a point cloud efficiently.
- LiDAR: LiDAR systems also use laser technology to measure distances and generate three-dimensional point clouds. They are often used in mobile mapping systems or building scanners.
- Photogrammetry: In photogrammetry, overlapping photos are analysed to calculate spatial structures. The image data is used to generate a point cloud that describes the geometry of the captured object.
What information does a point cloud contain?
A point cloud first describes the geometry of an object.
Typically, each point contains:
- Position
- X coordinate
- Y coordinate
- Z coordinate
- Intensity values: Many laser scanners also store information about the reflective properties of a surface.
- Colour information: By combining the data with camera images, RGB colour values can be assigned to individual points.
This creates realistic digital representations of buildings.
What does a point cloud look like?
A point cloud does not consist of surfaces or building components, but only of individual points in space.
When millions of these points are viewed together, they form a precise digital representation of a building.
For example, the following elements become visible:
- Rooms
- Walls
- Windows
- Doors
- Stairs
- Facades
- Technical systems
The point cloud represents the actual condition of a building, including all visible details.
Point Cloud vs. Mesh/BIM
- Point cloud: Raw data in the form of discrete points, ideal for derivations and findings.
- Mesh (OBJ/PLY/STL): A triangular network for visualisation or 3D printing, with little semantic information.
- BIM (IFC): A semantic model with building components and attributes, suitable for processes and operations.
- Best practice: Point cloud -> optional mesh -> scan-to-BIM, depending on the objective.
FAQ
What level of accuracy is realistic with point clouds?
Terrestrial laser scanning usually achieves millimeter to low-millimeter accuracy. Mobile LiDAR typically achieves centimeter-level accuracy, depending on the setup, environment and QA process.
How large should point clouds be?
As dense as necessary and as lean as possible. The resolution should be aligned with the intended use, while areas requiring more detail can be scanned more densely.
How do I use point clouds in CAD/BIM?
Reference the point cloud in formats such as E57, LAS or LAZ, set section planes and derive geometry. For BIM, use scan-to-BIM with clean classification in IFC and documented tolerances for CAD and BIM workflows.