Conflicting sphere target distances Faro Scene 2022.1

[lsf]

And the spheres after registration:
Type Name Scan X[m] Y[m] Z[m] R_d[m] R_X[m] R_Y[m] R_Z[m] R_X'[m] R_Y'[m] R_Z'[m] X~[m] Y~[m] Z~[m]

What does this data mean? Does it mean that PointCab has pushed the spheres around and now all of them are in the same coordinate system?

[VXGrid]

The worst sphere is S6 in Santana005, Yesterday I was already woundering if the glass and refraction is bleeding into the scan estimation.

Yes absolutely, this one cannot be used because it goes through the glass and based on my test results, I am always ignoring such spheres. Below is an image for other folks who are following along.

How does PointCab generate the depth map and what does it mean, how to understand that image?

The depth map is in this case just the distance between the scanner center and the depth to any point, visualized as a rolling colour gradient.
I think the steps were here blue to red equals one metre.

And the spheres after registration:
Type Name Scan X[m] Y[m] Z[m] R_d[m] R_X[m] R_Y[m] R_Z[m] R_X'[m] R_Y'[m] R_Z'[m] X~[m] Y~[m] Z~[m]

What does this data mean? Does it mean that PointCab has pushed the spheres around and now all of them are in the same coordinate system?

These are the parameters after registration, so XYZ is of course the world coordinate, R_ are the residuals and the last are the values of the inclinometer

[lsf]

Please see the results in the attached image or in https://docs.google.com/spreadsheets/d/ ... ingle=true

In summary, in "200mm Sphere Setting - before registration" case PointCab gives better results but it is not apples to apples comparison, because in Scene I used 203.2mm setting. In "203.2mm Sphere Setting - before registration" case while Scene has more values labeled as 'tighter', Scene also has more values that are larger than 1mm, but PointCab has larger 'largest variance'. The average difference between PointCab and Faro is 0.15mm. The "203.2mm Sphere Setting - after registration" case shows that results show pretty much no difference, so in this particular project, the precisions did not get worse.

I really thought that Scene has some mathematical flaw in sphere detection, but it seems that PointCab has the same issue, otherwise how can we have 3.3mm difference between same targets from different scans. This exercise has not yet answered that question.

landmeterbeuckx analysis shows that the spheres are actually 202mm so I wonder if results would be better if this size was used.

[lsf]

@VXGrid
I learned a bit about PointCab from some videos, and this one at this time said https://youtu.be/exE9_qG-P0U?t=320 that it will automatically find the diameter. What diameter value do you get when using that feature, is it same as one landmeterbeuckx got?

I've fitted some spheres in Riscan and found a diameter of 0.202m which is 7.9527 inches.

[lsf]

I've fitted some spheres in Riscan and found a diameter of 0.202m which is 7.9527 inches.

Lieven, do you think that Riscan is more powerful software for registration compared to Faro Scene?

[landmeterbeuckx]

I've fitted some spheres in Riscan and found a diameter of 0.202m which is 7.9527 inches.

Lieven, do you think that Riscan is more powerful software for registration compared to Faro Scene?

Definately but it is software made for a Riegl.

You can import other scans but only can only import a certain type of files like ptx, laz,... There isn't a batch process to import all at once so it has to be done manually.

I only use Riscan for my Riegl and the odd seperate scan from my tx5 if needed.

My take on this is that
1 spheres do shrink and expand in sun-no sun- cold-.... conditions no matter what the seller tells you
2 you cannot have the same results on distances when placing your instrument between spheres to compare
3 a faro but also a leica, zf, riegl, .... will only be accurate within a few mm. Specs are specs and ideal conditions.
4 when building something to fit in your scanned situation there will be always a tolerance. One that says all is pre febricated (except for cnc) or welded to the 0.5mm is telling porkies.
5 use of a 1" ts will help but even with a 1" there will be errors.

You have to take in account all of this and just take the 2-3mm overall error in consideration when doing a project. Or you'll have to go with laser trackers and sub-mm handhelds which imho for your stuff are of no use.

[VXGrid]

@VXGrid
I learned a bit about PointCab from some videos, and this one at this time said https://youtu.be/exE9_qG-P0U?t=320 that it will automatically find the diameter. What diameter value do you get when using that feature, is it same as one landmeterbeuckx got?

I've fitted some spheres in Riscan and found a diameter of 0.202m which is 7.9527 inches.

The diameter, which is preset in the settings, not just a random one.
From alignment theory the idea is to compare a measurement to a real known fact.
Real sphere size vs measured points on the sphere and then estimated size.
Distance between Sphere A and B must be identical in scan1 and scan2.

The image from Lieven shows very nicely that the point distribution on the sphere is varying up to 2mm, while in the menu we see that the deviation is only 0.4mm.

Since spheres are measured from different sides, the sphere diameter detected can vary if measured from the front or back, so we prefer to put the error on the sphere in the model, rather than the scan fitting.

[SAttaya]

I have only one single sphere radius defined in the settings and it is 4" for 8" spheres therefore I think this should not be the problem, because left and right scan is okay.

200mm is not 8"
145mm is not 6"

as in, who makes 8" spheres?
multiple sources for 200mm spheres, Ted K & LSA/E

8" = 203.2mm (8 * 25.4000000000000mm)

[lsf]

Regarding

200mm is not 8"
145mm is not 6"

8" = 203.2mm (8 * 25.4000000000000mm)

you are correct and VXGrid kindly checked the 0.2032m diameter:

The automatic detection with 0.2032 m was a lot better, with that it found all automatically, with 0.2m I clicked a lot of them myself (which doesn't change anything in accuracy)

This topic is still on my todo list because I got carried away with math. Let me explain. The core exercise here is to understand why the 3D distances between the same spheres across multiple different scans (individual scans, not registered ones) are not the same. So I went back to the very first step which is matching the spheres in the point cloud. After different experiments it seems that Scene matches the green sphere with some area of the point cloud sphere (why do I think so, because I changed the diameter in the settings, and the green sphere always matches with some points and not with the others), which goes against my intuition. If I was a C++ developer who needed to solve the sphere matching algorithm, I would try to match the sphere between all the points and have a mismatch everywhere instead of aligning the green sphere such that it matches the cloud points only at some part of the surface. With my approach even if the sphere size in the settings is larger/smaller (some range of course), it would still determine the center point correctly and if the green sphere size in the settings is say 1mm smaller than the size in the point cloud, I could see how the cloud points are offset from the green sphere at all surface areas evenly. It might even help in cases when spheres are slightly oval.

So what I got carried away with is I found mathematical algorithm that calculates the radius of the sphere from 4 points of the surface. If the sphere has say 1000 points on the surface, the thought is to take the first 4 points (1 to 4) and calculate the radius, then take points 2-5 etc. So I would end with bunch of radiuses. I would calculate the normal distribution and get the 'bell' curve. My statistical knowledge is a bit rusted but, then I believe I need to take +- 1 standard deviation from the mean and disregard all the other radiuses and outliers. I would then need do calculate another bell curve from this filtered data. I am not sure how many times I would need to do that, but at some point I should get a good 'statistical' radius. I have to think that is how Riscan works?

I've fitted some spheres in Riscan and found a diameter of 0.202m which is 7.9527 inches.

[Least]

I dont think manufacturers specs should be taken too literally when it comes to mid-range scanners.

You might find this article interesting, it explains sources of errors, distance and angular, targetting errors and suitable testing procedures.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8314184/