Consistent Monocular Depth Estimation with Contact Region Boundary-Aware Refinement

While contemporary monocular depth estimation (MDE)methods achieve remarkable overall acacy, they consistently produce er-roneous depth discontinuities at object contact regions, particularly be-tween objects and supporting surfaces. In this paper, we address this crit-ical limitation by presenting a boundary-aware monocular depth estima-tion framework that enforces depth continuity at contact areas throughthe principled exploitation of contact boundaries as explicit structuralpriors. Specifically, we propose a boundary detection and filtering modulethat explicitly identifies object contact regions, yielding a novel boundary-aware representation that enables depth-consistent learning at contactareas. Furthermore, we introduce a boundary-aware feature fusion strat-egy that seamlessly incorporates contact boundary priors into the depthdecoding process, effectively rectifying the persistent discontinuities thatelude existing approaches. Our framework further supports interactiverefinement, allowing users to manually specify missing contact bound-aries for controllable depth correction. Extensive experiments on threeunseen benchmarks with dense object interactions demonstrate the ef-fectiveness of our approach, consistently outperforming baselines withparticularly pronounced gains in contact regions. Code is available athttps://github.com/abai969/contact-depth-refinement.git