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May 13, 2026Journal of Institute of Control Robotics and Systems

Efficient Self-supervised Monocular Depth Estimation via Knowledge Distillation and Light-weighted Attention

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Authors

SHSeung-Min HanSKSeung-Hyun Kong

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Overview

Randomized trial improves depth estimation accuracy in autonomous systems, suggesting enhanced training stability.

Key Points

  • This research aims to develop a method for improving the accuracy and stability of self-supervised monocular depth estimation.
  • Utilized knowledge distillation from a foundation model to enhance training stability.
  • Introduced a lightweight attention module to strengthen global spatial representation.
  • Reduced model parameters by 40% and FLOPs by 20% during implementation.
  • Achieved improved absolute relative error (abs_rel) performance compared to baseline methods.
  • Demonstrated enhanced prediction accuracy with reduced computational costs through the proposed framework.

Cite This Study

Han et al. (2026) studied this question.

synapsesocial.com/papers/6a04141c79e20c90b44444abhttps://doi.org/10.5302/j.icros.2026.26.0045
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Self-Supervised Monocular Depth Estimation Based on Differential Attention2025 · 2 citations
  2. 2Monocular Depth Estimation via Self-Supervised Self-Distillation2024 · 8 citations
  3. 3Robust Lightweight Depth Estimation Model via Data-Free Distillation2024 · 2 citations
  4. 4Self‐Supervised Monocular Depth Estimation: A Review of Paradigms, Evaluation Protocols, and Technical Advances2026
  5. 5Lightweight Monocular Depth Estimation with Local Feature Enhancement Modules and Guided Data Augmentation2026