Scanning larger objects

[curious aardvark]

you got any example that it doesn't ?

using a modern hi-resolution cameras and autodesk photorecap ?

I've done a few scans recently - with solid colour objects and qlone - a photogrammetry app.
it's not an issue.
Photogrammetry works more on the background colours than the colours of the actual object.
Which you'd know, if you'd ever done any.
It's frequently used for buildings - objects famous for large blocks of solid colour.

It does seem that you just automatically say the opposite of everybody else. Without any experience or evidence.

[Martin_au]

you got any example that it doesn't ?

using a modern hi-resolution cameras and autodesk photorecap ?

I've done a few scans recently - with solid colour objects and qlone - a photogrammetry app.
it's not an issue.
Photogrammetry works more on the background colours than the colours of the actual object.
Which you'd know, if you'd ever done any.
It's frequently used for buildings - objects famous for large blocks of solid colour.

It does seem that you just automatically say the opposite of everybody else. Without any experience or evidence.

Quite a bit.
First, please don’t try and reverse the burden of proof. That’s a bad faith method of argument.
Second, I apply photogrammetry in both my work and as a side interest on a regular basis (daily to weekly). I work regularly with the state’s museum, the university’s palaeontology group, and others groups in research and industry. I’ve done photogrammetry of objects down to sub 5mm, and up to full site captures. I use a range of equipment, typically a high end camera, but also drones and more recently the Leica GS18i.
Third, the entire principle of photogrammetry (sfm-mvs) works by matching identifiable points in photos. No identifiable features, no matches.
Fourth - one of many: https://www.sciencedirect.com/scienc...96207415000230

One of the factors that determine the data quality produced by targetless photogrammetric techniques is the feature richness of the surface being captured. The Structure-From-Motion and Multiple View Stereovision (SFM-MVS) pipeline is no exception to this rule as it relies on the ability to identify corresponding points within a collection of unordered images

Furthermore, the complete artefact's reconstruction without the use of NFPs proved to be problematic as SFM-MVS failed in image spatial alignment due to lack of features. Thus, we repeated the data collection phase by including additional target objects in order to provide features. This time, the alignment was successful but the poor quality of the reconstruction (e.g. noise presence, mesh incontinuities, etc.) indicates an unfriendly SFM-MVS surface.

If you like I can drop 50 or so links to photogrammetry papers that describe how the process works.