Distill Any Depth: Distillation Creates a Stronger Monocular Depth
  Estimator
Monocular depth estimation (MDE) aims to predict scene depth from a single
RGB image and plays a crucial role in 3D scene understanding. Recent advances
in zero-shot MDE leverage normalized depth representations and
distillation-based learning to improve generalization across diverse scenes.
However, current depth normalization methods for distillation, relying on
global normalization, can amplify noisy pseudo-labels, reducing distillation
effectiveness. In this paper, we systematically analyze the impact of different
depth normalization strategies on pseudo-label distillation. Based on our
findings, we propose Cross-Context Distillation, which integrates global and
local depth cues to enhance pseudo-label quality. Additionally, we introduce a
multi-teacher distillation framework that leverages complementary strengths of
different depth estimation models, leading to more robust and accurate depth
predictions. Extensive experiments on benchmark datasets demonstrate that our
approach significantly outperforms state-of-the-art methods, both
quantitatively and qualitatively.
67bfd735ca6e3c22b6de43c7
Xiankang He
67bfd735ca6e3c22b6de43c8
Dongyan Guo
67bfd735ca6e3c22b6de43c9
Hongji Li
67bfd735ca6e3c22b6de43ca
Ruibo Li
67bfd735ca6e3c22b6de43cb
Ying Cui
67bfd735ca6e3c22b6de43cc
Chi Zhang
Distill Any Depth: Distillation Creates a Stronger Monocular Depth
  Estimator
Monocular depth estimation (MDE) aims to predict scene depth from a single
RGB image and plays a crucial role in 3D scene understanding. Recent advances
in zero-shot MDE leverage normalized depth representations and
distillation-based learning to improve generalization across diverse scenes.
However, current depth normalization methods for distillation, relying on
global normalization, can amplify noisy pseudo-labels, reducing distillation
effectiveness. In this paper, we systematically analyze the impact of different
depth normalization strategies on pseudo-label distillation. Based on our
findings, we propose Cross-Context Distillation, which integrates global and
local depth cues to enhance pseudo-label quality. Additionally, we introduce a
multi-teacher distillation framework that leverages complementary strengths of
different depth estimation models, leading to more robust and accurate depth
predictions. Extensive experiments on benchmark datasets demonstrate that our
approach significantly outperforms state-of-the-art methods, both
quantitatively and qualitatively.
Cross-Context Distillation using global and local depth cues, combined with multi-teacher distillation, improves zero-shot monocular depth estimation.
zero-shot MDE depth normalization distillation-based learning global normalization pseudo-label distillation Cross-Context Distillation multi-teacher distillation 3D scene understanding