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Point Cloud Conformance

Author: Lewis Graham, May 20, 2021

I have been in the business long enough to watch LIDAR point density go from single line profilers to perhaps a point every few meters to the incredible density we get now with True View 3DIS

Back in the Day…

In the beginning, the technical focus of the community was firstly on vertical accuracy and secondly vegetation penetration.  If you think about it, a vertically accurate point on the ground every few meters is a big step up from boots on the ground topo collection.  No wonder the Federal Emergency Management Agency (FEMA) was one of the first to mandate LIDAR for flood plain mapping; LIDAR made cyclic updates of Digital Flood Insurance Rating Maps (DFIRM) a reality.  Figure 1 shows a LIDAR project from circa 2005 with a single flight line nominal point spacing (NPS) of 1 m (~3 feet).  This was a fabulous data set for its time. 

Keenland Point Cloud Data Set Circa 2005
Figure 1: "Keenland" Data Set, circa 2005

Nowadays we use LIDAR for direct 3D imaging with a variety of applications as broad as your imagination.  However, we really have not moved much beyond the original assessment of LIDAR data.

We now use LIDAR for myriad tasks beyond figuring a topo map.  We routinely collect transmission/distribution wires, collect stockpile volumetrics, model rail, monitor construction sites, etc.  

What is Point Cloud Conformance?

One of the major issues we are concerned with is what I call point cloud “conformance.” Conformance is a measure of how well the point cloud fits the true object space.  For example, if we are modeling something lumpy like a gravel or coal stockpile, do we have some amorphous blob or does the point cloud closely follow the true shape of the object being modeled? 

For example, Consider a row of HVAC units beside our new GeoCue main building (Figure 2).  These units are rectangular with some transparency to the interior via the fan guards.  We would expect to see rectangular peaks in the LIDAR data with a few stray interior points across the mid-section from penetrating rays.

Rows Of HVAC Units
Figure 2: Row of HVAC Units

If we examine data from a True View 515 3D Imaging System (3DIS), we see just what we expect (Figure 3).  These data were captured during a LIDAR QC flight at 75 m Above Ground Level (AGL). 

HVAC Units Scan By True View 515 3DIS 75m AGL
Figure 3: HVAC unit scan by True View 515 - 75m AGL

We are testing a new LIDAR system (let’s call it X and it is not a True View 3DIS!) that shows poor performance in the conformance area.  Consider the same scan from X (that is, a 75 m AGL, all flight lines scan).  

As you can see in Figure 4, the conformance of system X is really terrible.  The rectangular HVAC units are reduced to a series of hay piles!  I would be reticent to use this system for anything except a few perhaps very well known scenarios (clear area, hard, flat surface?) due to this obvious gross inaccuracy in conformance.

System X HVAC Row Conformance
Figure 4: System X HVAC row conformance

An interesting note is the vertical accuracy of system X measured on flat, hard surfaces is in the neighborhood of 4.5 cm Root Mean Square Error (RMSE).  By the old vertical accuracy standards, a fine LIDAR sensor.  But we know better!

The takeaway message here is to look holistically at LIDAR, camera and 3DI systems.  Vertical accuracy, expressed as RMSE against a set of check points, is a very important LIDAR parameter but if conformance is as bad as shown in Figure 4, do you really have a useable system?  I would say unequivocally, no.  We will be examining various sensor characteristics over the next few issues of the Bulletin that will give you some additional pointers as to what to look for in a sensor. 

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