deep-video-mvs VS colmap

If deep learning is an option, then you might want to check out http://zak.murez.com/atlas/, https://zju3dv.github.io/neuralrecon/, https://github.com/ardaduz/deep-video-mvs and the references therein. These methods can be better than classical ones, especially if overfitted on a specific type of scene.

Not saying its easy to use, but there is an application gui and it is free: https://github.com/colmap/colmap

Colmap (Model folders (BIN and TXT), dense workspaces, NVM, PLY)

See colmap for example https://colmap.github.io/

You're looking into a problem called photogrammetry, and a well-studied one at that. I'd recommend looking into "shape from motion" (sfm); specifically techniques that do "dense reconstruction." I'd recommend COLMAP to start with. It does pose estimation from images (e.g. you point it at a bunch of images and it will figure out the relative poses of the cameras that took them), as well as sparse and dense reconstcution.

COLMAP builds dense meshes from a collection of cameras https://colmap.github.io/

I’ve seen references to using COLMAP (https://colmap.github.io/) to estimate camera position/pose, e.g. here

Classical photogrammetry, where I agree with u/tdgros that the way to go is https://colmap.github.io/. There are actually better variants in literature but nothing is more reliable and user-friendly than COLMAP. This will give you a very precise point cloud, that can be meshed if needed.