colmap VS OpenSfM

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.

OpenSfM (JSON)

You're kidding right? There are lots of coders on github with open-sauce Python. The basic algorithm being used now is Structure-from-Motion (SfM). Photogrammetry started with projecting a red stripe light on the object, taking the photo, and manually stitching together common points. That uses edge detection, another algorithm. Everybody knows the Z-depth calculation algorithm, and that is starting to be done with AI. Sony is using some scrolling parallax algorithm with their photogrammetry software. It looks like Reality Capture can calculate the camera positions and angles first, then use photon tracing rather than ray tracing to determine the point cloud. OpenSfM https://github.com/mapillary/OpenSfM

OpenSfM is written in Python and JS and pretty easy to understand.