The input to this are two things - images, and camare poses. The camera poses tell you where each camera was in 3D space (and some of its properties).
The training takes this information, to make a 3D model out it, visually matching all your photos.
COLMAP can still be quite expensive & a hassle sadly, order half hour, as opposed to seconds. There are modern alternatives like https://lpanaf.github.io/eccv24_glomap/, or even deep learning based systems like https://github.com/naver/dust3r
This is definitely still a big blocker to adoption. The goal is to get to a more all-in-one system. The splatting optimization can also help align cameras, if they don't start out entirely random, so any system to quickly provide a good "initial guess" will help here. At least for mobile devices, initialization from ARCore / ARKit poses should be enough.
Keep an eye out :)