I’ve been having a bit of browse through the forest of Open Source computer vision “applications” as of late. I quote “application” to mean packages with a high level of integration — either end-user applications, or high-level libraries which are one step from a demo application — in contrast to say, OpenCV or ANN which are very much at the nuts and bolts level.
I’ve made no effort to evaluate these packages, just to call out that they exist. I’m ambivalent to academic work which leads to large monolithic packages — I guess I just want things to be a bit more atomic so the quality visualizer from this project can be mated to the descriptor code from that project etc without completely knocking the two apart.
But on the other hand, these are wonderful repositories for knowledge and code — code for constructing and decomposing fundamental matrices, bundle adjustment, etc. There’s already far too much re-re-re-engineering of algorithms — although I’m as guilty as others, as re-implementation is a great way to learn an algorithm inside and out.
Of course, at this point I should mention OpenSLAM which aims to be a repository (in the philosophical and literal sense) for SLAM algorithms and test datasets.
- Insight3d is a full 3D reconstruction application. Images in one side, point cloud (and geometric model?) out the other.
- PTAM (Parallel Tracking and Mapping) is a SLAM-like toolkit/application designed for augmented reality, coming out of the Machine Vision group at Oxford.
These are a bit more like libraries, but I’ll call them out:
- Christopher Zach has published Simple Sparse Bundle Adjustment (SSBA). He’s also provided a full Structure for Motion toolkit.
- SBA is another sparse bundle adjustment toolkit “from Manolis Lourakis at the Institute of Computer Science of the Foundation for Research and Technology – Hellas (FORTH) in Heraklion, Crete, Greece.”