Making a Detailed Point Cloud Without Spending All Day on It
Point clouds are notoriously unwieldy things. With millions of individual points converging to create a model, it can be a time-consuming task to convert a point cloud into a usable rendering.
But what if it wasn’t so time-consuming?
One of the exhibitors at the SPAR 3D Expo and Conference 2016, GeoSLAM, has a software platform that promises to make point clouds much easier to work with.
Point Cloud Rendering on the Cloud
GeoSLAM’s software is designed to create and organize generic point clouds based on information gathered from a profiling laser scanner while reducing the time it takes to produce this rendering.
It’s intended as a tool for mapping the interiors of buildings to produce rapid as-built floor plans.
So how does it work?
- The software ships with its own laser scanner, either the ZEB1 or the ZEB-REVO, which can be mounted on a mobile platform to traverse the entire survey environment. Each scanner can record upwards of 40,000 points/second.
- This raw data is then uploaded to the GeoSLAM cloud, where simultaneous localization and mapping (SLAM) software sorts points into a fully registered point cloud. The software uses a color-coded gradation system to mark heights and facilitate interpretation.
Here’s a look at how it works:
Using Point Clouds for Indoor Mapping
Scanning a building’s interior to create a point cloud isn’t a new concept, but it’s a very useful one. As part of GeoSLAM’s exhibit, the company was showing off a point cloud rendering of the conference hall that it had created just that morning.
GeoSLAM did a project very similar to its scan of the conference hall using the Lincoln Memorial as a subject. The entire project only took a few hours. (Image courtesy of GeoSLAM.)
According to Richard Durrant, chief engineer at GeoSLAM, scanning the entire hall took less than ten minutes while the rendering took a few hours. The memorial registered a comparable time. Ordinarily, scanning the hall by itself would have taken a few hours and rendering the point cloud afterward would have taken even longer.
For more information, check out the GeoSLAM website.