Semi-Global Stereo Matching with Monocular Disparity Guess
A stereo depth estimation project that combines classical and learning-based approaches: a monocular disparity prediction is used as the initial guess, which Semi-Global Matching (SGM) then refines into the final disparity map. Seeding SGM with the monocular estimate substantially improved accuracy over the baseline, reducing mean squared error by 75%.
Mohammadhossein Akbari Moafi
AI Researcher and Engineer passionate about building practical, efficient intelligent systems across vision, multimodal, and knowledge-driven applications.