depth-anything-any-condition

Extend monocular depth models to handle adverse conditions (weather, darkness, sensor noise) using only 540K training samples. Applies perturbation-based consistency learning and spatial constraints to maintain robust depth prediction across challenging real-world scenarios.

Independent GitHub snapshot · Automated classification · Original documentation governs