dust3r

You could try generating per-view depth maps, going to a point cloud and meshing from there. (I suspect splats may reduce your accuracy as an intermediate.) I’m not aware of a fully-baked workflow for that — though it may exist. The first step has gotten really good: the recent single-shot AI models for depth are pretty visually impressive (I don’t know about metric accuracy). The ones I’m aware of are DUST3R and the newer MAST3R: https://github.com/naver/dust3r https://github.com/naver/mast3r Good luck!

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.