Boosting 6D Object Pose Estimation via Monocular Depth Cues

Abstract

We present an RGB-only method for 6D object pose esti-mation that leverages monocular depth cues from a single image. Al-though monocular depth estimation has advanced substantially, its pre-dictions remain scale-ambiguous and locally unreliable, limiting theiruse in metric pose refinement. We address this gap by closing the loopbetween depth correction and pose refinement: monocular depth is notonly a regularizer but is iteratively calibrated and filtered using pose-induced geometric consistency, enabling stable metric pose updates fromRGB alone. We propose a dynamic depth outlier removal module basedon metric consistency and infer object pose from dense 2D correspon-dences. Both components are embedded into a recurrent optimizationloop, enabling iterative depth correction and pose refinement. Experi-ments on seven BOP datasets demonstrate state-of-the-art performanceamong RGB-only methods, without requiring real depth input.