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Related Experiment Video

Updated: Jan 29, 2026

Procedure for the Development of Multi-depth Circular Cross-sectional Endothelialized Microchannels-on-a-chip
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MogaDepth: Multi-Order Feature Hierarchy Fusion for Lightweight Monocular Depth Estimation.

Gengsheng Lin1, Guangping Li1

  • 1School of Information Engineering, Guangdong University of Technology, Guangzhou 510000, China.

Sensors (Basel, Switzerland)
|January 28, 2026
PubMed
Summary
This summary is machine-generated.

MogaDepth enhances monocular depth estimation by focusing on mid-order semantic features. This lightweight architecture improves accuracy and efficiency for real-world applications like autonomous driving.

Keywords:
edge deviceslightweight modelmonocular depth estimationmulti-order feature interactionsself-supervised

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Area of Science:

  • Computer Vision
  • Machine Learning

Background:

  • Monocular depth estimation is vital for autonomous driving and augmented reality.
  • Existing lightweight methods often overlook crucial mid-order semantic feature interactions.

Purpose of the Study:

  • To introduce MogaDepth, a novel lightweight architecture for improved monocular depth estimation.
  • To enhance the representation of mid-level features for greater depth accuracy.

Main Methods:

  • Developed the Continuous Multi-Order Gated Aggregation (CMOGA) module to enhance mid-level features.
  • Introduced MambaSync for efficient global-local feature communication.
  • Proposed MogaDepth, a lightweight and expressive network architecture.

Main Results:

  • MogaDepth achieved competitive or superior performance on the KITTI benchmark, improving error metrics.
  • Outperformed existing methods on the Make3D benchmark, demonstrating robustness to domain shifts and challenging scenarios.
  • Achieved up to 13% faster inference on edge devices without performance compromise.

Conclusions:

  • MogaDepth offers an effective and efficient solution for real-world monocular depth estimation.
  • The proposed CMOGA and MambaSync modules significantly contribute to improved depth accuracy and efficiency.