mtngld/lsim

The 'lsim' project represents an early implementation (circa 2017) of self-supervised monocular depth estimation. While the concept of using stereo pairs for training to enable monocular inference was innovative at the time, the project has effectively been abandoned for years (0 velocity, 7 years old). With only 18 stars, it never achieved significant traction or community momentum. In the current landscape, this project is entirely obsolete. It is displaced by modern 'Foundation Models' for depth estimation such as Depth Anything (Li et al.), MiDaS (Intel Labs), and ZoeDepth, which offer vastly superior zero-shot generalization. From a competitive standpoint, there is no moat; the code likely relies on deprecated libraries (TensorFlow 1.x era), and the underlying architecture lacks the transformer-based or advanced CNN improvements seen in more recent repositories like 'monodepth2' by Niantic Labs. Frontier labs and major cloud providers (Google, AWS, Azure) now offer robust depth-sensing APIs as part of their vision suites, leaving little room for niche, unmaintained research implementations. This serves only as a historical reference implementation for researchers looking at the evolution of self-supervised vision.