REALITY CAPTURE KNOWLEDGE · POINT CLOUDS
Yes.
A point cloud can look convincing at first glance while still containing technical problems.
One of the first things I check is how well neighbouring scan positions align. Poor registration can create duplicated surfaces or ghosting, meaning that geometry representing the same physical object does not correctly coincide.
Problems can also originate earlier in the workflow. Incorrect field acquisition or positioning issues, including the use of inappropriate RINEX data during PPK processing, can affect the spatial reliability of the resulting dataset.
The opposite is also true. A point cloud may look less impressive because it has a lower point density, but still be technically excellent if the geometry is reliable and the required information can be extracted.
Visual appearance and geometric reliability are not the same thing.
SEE IT IN PRACTICE
In an elevator shaft verification project, registered point cloud data was used to identify and quantify geometric deviations that were preventing installation of the elevator system.
ABOUT THIS KNOWLEDGE SERIES
Reality Capture Knowledge is where I share short, practical answers to questions that come up across reality capture, point cloud processing and geospatial workflows.
The answers are based on more than 15 years of hands-on experience working with spatial data, field acquisition and point cloud processing.
Roland Kriston
Independent Reality Capture & Geospatial Specialist