Researchers propose a diffusion-based feature restoration approach to improve monocular depth estimation by treating encoder features as degraded versions of ideal features.

Study identifies untapped potential in current encoder-decoder architectures used for monocular depth estimation (MDE)

Novel InvT-IndDiffusion module developed to restore and enhance pretrained encoder features for better depth prediction

Approach reformulates depth estimation as a feature restoration problem rather than direct depth prediction

Research reveals that improving encoder features significantly impacts overall prediction accuracy in MDE tasks