Not the best example but this http://koanlabs.org/vignettes.html rock is ~5 cm tall and my best guess is the minimum detectable feature size is about 10 microns.
EDIT: Sorry to be clear the definition of easy here is "as easy as most photogrammetry", to get good results you will universally do a lot of knob twiddling, waiting and cleaning up broken bits of mesh unless all you do is scan rocks. Scanning rocks is easy.
"The devil is in the details" and "talk is cheap" are common expressions for a reason
But I have found that smartphones give very inconsistent results (probably due to image"improving" algorithms...), see https://www.instagram.com/p/B1q1syMoHfG/
So I moved on to the raspberry pi + camera, which is much easier and better to control as you need very, very consistent images...
It worked surprisingly well, but was unfortunately discontinued. Since it was cloud based the app no longer works :(
I wonder if there are any similar apps available now?
Edit:
Here is the source for the claim: https://www.reddit.com/r/OpenScan/comments/gfottc/10_micron_...
It seems this might be the theoretical error that you can get when the system can identify a feature perfectly, and when it gets a usable reflection from that point at enough angles.
But this is not the case for most points on any real object (i.e. not a chalk-coated gauge block), so you're definitely not going to get a model where the maximum error is 10 microns.
The results are certainly impressive, but hackster.io is taking liberties with that headline. It's not a realistic accuracy and the author of the project doesn't really seem to be making that claim.
The author makes the 10 micron claim in several other places, including https://www.reddit.com/r/photogrammetry/comments/k5dbtk/auto... about a totally different model. That thread discusses ways to quantify accuracy and generally agrees that the rig should not be capable of such feats.
But then there are images like https://www.reddit.com/r/OpenScan/comments/ls17tt/closeup_of... in which his scans are picking up the silk screening on the surface of the board, which at least lends some credence to the idea that he's getting 10 micron accuracy.
Anyway, feel free to discuss and ask me anything about it :)
[1] http://www.imagescienceassociates.com/mm5/pubs/What_is_an_MT...
[2] https://iopscience.iop.org/article/10.1088/1361-6501/aa9aa0/...
and the results from that scan along with the cloud processing photogrammetry workflow can be seen in the video at https://www.youtube.com/watch?v=EhvFq-OYa1g
The author says he wants a one-click solution to getting a 3d model of a scanned object. I think that's a great goal - as easy as hitting the green button on a photocopier, except now three dimensional. From the automated picture-taking and that cloud processing workflow, this project is already remarkably close.
I think this project is about making a bunch of photos of the object and share them with SAMBA. Then you can load them into meshroom or another software. It's not all in one package.
1. Soldering
2. Wiring all parts
3. User Interface
That third one ("User interface") shows how to use the software. From just glancing at it, it seems like it'll be ok.
At the very end there is a list of future things that I'm guessing will also be added to the manual:
4. Workflow
5. Build - 3D Printed Version
6. Build - CNC version
7. Photogrammetry Software - Overview
8. Post Processing
Hopefully these last sections are also filled out in the near future, as they sound pretty useful. :)
Is there an actual reason for this? Given the Raspberry Pi is a computer too, shouldn't it be able to compute the results as well, only slower than a proper PC?
Even on a good desktop processor, it can take hours for a small scene. You really want a GPU for it.
No one wants to wait two weeks while their r-pi converts a point cloud to a mesh.
