title: Distill Any Depth: Distillation Creates a Stronger Monocular Depth
    Estimator
summary: 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.
paper_authors._id: 67bfd735ca6e3c22b6de43c7
paper_authors.name: Xiankang He
paper_authors._id: 67bfd735ca6e3c22b6de43c8
paper_authors.name: Dongyan Guo
paper_authors._id: 67bfd735ca6e3c22b6de43c9
paper_authors.name: Hongji Li
paper_authors._id: 67bfd735ca6e3c22b6de43ca
paper_authors.name: Ruibo Li
paper_authors._id: 67bfd735ca6e3c22b6de43cb
paper_authors.name: Ying Cui
paper_authors._id: 67bfd735ca6e3c22b6de43cc
paper_authors.name: Chi Zhang
paper_title: Distill Any Depth: Distillation Creates a Stronger Monocular Depth
    Estimator
paper_summary: 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.
paper_ai_keywords: Monocular depth estimation zero-shot MDE normalized depth representations distillation-based learning global normalization pseudo-labels Cross-Context Distillation local depth cues multi-teacher distillation benchmark datasets