OpenMVG (open Multiple View Geometry) VS Dlib

Try to find a community that will know more about openMVG, like the discussion tab on their github.

OpenMVG (JSON, NVM, PLY)

I find openMVG very decent, FTXUI might be a good one and nlohmann's json library is also pretty nice. I don't really know of any project that strictly adheres to the core guidelines, except maybe for some of Jason Turner's (sample) projects.

I am looking for some pointers to papers/open-source software that can do fast and accurate depth estimation from multi-view videos. I am aware of colmap and OpenMVG software but was curious to know if there are others out there that outperform these. I am also interested in any state-of-the-art for depth estimation using unsupervised or self-supervised neural networks.

OpenMVG is super cool, and the project lead Pierre is very generous with his time.

Meshroom is just a Python wrapper for VSfM. OpenMVS seems like the package most customizers are starting to use. https://github.com/openMVG/openMVG

dlib

The apparent gender estimates from photos are using dlib, and I really ought to get what I'm doing cleaned up in such a way that other people can use it easily.

I really like dlib's code https://github.com/davisking/dlib

Additionally, C++ may be used for extremely high levels of optimization even for cloud-based ML. Dlib and Kaldi are C++ libraries used as dependencies in Python codebases for computer vision and audio processing, for example. So if your application requires you to customize any functions similar to those libraries, then you'll need C++ knowhow.

If you know C++, you don't need anything else. Go and learn APIs for C++ libraries. If you're into DSP, why not study Dlib?.

The data is mostly in this spreadsheet. The apparently facial gender estimates are made with Dlib. The mental health assessments are from Beck's Depression Inventory and the Snaith-Hamilton Pleasure Scale. The graph is made with gnuplot.

The plugin uses dlib library with a very fast HOG detector for both face recognition and detector following the relative examples.

The dlib facial recognition model thinks that I am now a distance of about 0.3 from where I started, which is far enough to start getting many false positive matches, but still within the design intent that different pictures of the same individual will be within 0.6 of each other.

Dlib's face recognition module thinks that I am about 0.25 units away from where I started; its design intent is that distinct individuals will be 0.6 or more apart, although in practice other people start showing up around 0.3.