[plafl]

I have been recently looking at several "3d scanning" from monocular images/videos solutions and although they make for impressive demos I'm afraid they are a few years away from robust results.

On one hand you have traditional photogrammetry based on classical image descriptors (from the beginning of the 2000s). A nice open source solution is Meshroom [1]. I have very little experience as I have just tinkered a little but I would say they work OK with geometry of medium complexity and detailed textures. They fail horribly with untextured objects, like for example a candle. They are semi-automatic: you upload some photos and run the pipepiline but for best results it's easy to tweak as there are a lot of knobs to try.

On the other hand you have these deep learning research papers. A lot of them have Colab notebooks you can try yourself (which is awesome, thank you). They can give you these very nice demos for some kind of objects (the ones we have a lot of training data, like human bodies), but they are not truly general and sometimes will generate artifacts. There is some opportunity here if they are integrated in a semi-supervised workflow, like this [2].

Anyway, just curious and not an expert.

[1] https://alicevision.org/ [2] https://keentools.io/products/facebuilder-for-blender