gflags VS colmap

Someone opened an issue at https://github.com/gflags/gflags/issues/76, to request for a feature of flag alias, and it has been opened for about 7 years.

git clone https://github.com/gflags/gflags.git

Google's command line flags library, known to the public as absl::Flags and formerly gflags, does not distinguish between --foo and -foo, these are both the flag "foo". Each flag has a unique name so there is never a short -f equivalent to --foo, and -foo can never mean -f -o -o.

The main design motivation of absl::Flags is that the flag definitions can appear in any module, not just main. Go inherits this. A quirk that Go did not inherit is gflags --nofoo alternate form of --foo=false.

This is all documented at https://gflags.github.io/gflags/#commandline, which is pretty much a literal export of the flags package documentation that a Google engineer would see internally.

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.