pixel-perfect-sfm VS colmap

Are there alternatives available to the SfM pipeline? https://github.com/cvg/pixel-perfect-sfm for instance takes a deep NN to predict surfaces in the dense step ( not needed for GSplats), is a trained approach for spare reconstruction thinkable, are there other SfM methods that are significantly faster or find more features?

If you are interested in camera pose estimation from images, then the state of the art baseline is still Colmap which is used by industry and research. You can checkout concurrent work like https://github.com/cvg/pixel-perfect-sfm. Should be a lot of tutorials and documentation for you to get it running.

Finally there is also https://github.com/cvg/pixel-perfect-sfm which does do some photometric/dense refinement, but I think it only works in the featuremetric space. So not exactly photometric.

I've stumbled upon this and that using DL, and will try to check to simultaneously evaluate them next to developing something using pySLAM. At least that's the current plan.

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.