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rust-cv
Rust CV mono-repo. Contains pure-Rust dependencies which attempt to encapsulate the capability of OpenCV, OpenMVG, and vSLAM frameworks in a cohesive set of APIs.
Currently, `vslam-sandbox` operates by taking all images to run at the command line. We should augment this by allowing images to be added through either drag-and-drop or a file chooser...
A lot of computer vision algorithms work on image pyramids so an implementation would be useful, either here or http://github.com/xd009642/ndarray-vision
To show the output of vSLAM and visual odometry algorithms, we need a crate that can efficiently take VBOs of points to display and render them. A crate should be...
See this link for details: https://borg.cc.gatech.edu/projects/ilba.html. Structureless bundle adjust is a technique that is already used in `vslam-sandbox` today. Unfortunately, the way that it does it is not efficient. It...
It would be good to have at least a simple BoW matching algorithm. Refer to [the wikipedia page](https://en.wikipedia.org/wiki/Bag-of-words_model_in_computer_vision) for more information. This is necessary to make the matching process go...
["VITAMIN-E: VIsual Tracking And MappINg with Extremely Dense Feature Points"](https://arxiv.org/pdf/1904.10324.pdf) is a paper that introduces several novel concepts. One of the things it does is create highly accurate meshes in...
["VITAMIN-E: VIsual Tracking And MappINg with Extremely Dense Feature Points"](https://arxiv.org/pdf/1904.10324.pdf) is a paper that introduces several novel concepts. One of those is a curvature based feature detection scheme. This scheme...
`cv-core` has a [`CameraModel`](https://docs.rs/cv-core/0.15.0/cv_core/trait.CameraModel.html) trait that should be implemented for fisheye cameras. A new crate should be created called `cv-fisheye` for this purpose, similar to the existing `cv-pinhole`.
We should create an indirect vSLAM application to test our algorithms and rally support in the community. It might also be useful to some to get 3d reconstructions from video...