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Updated: Jan 13, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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MsGf: A Lightweight Self-Supervised Monocular Depth Estimation Framework with Multi-Scale Feature Extraction.

Xinxing Tian1, Zhilin He1, Yawei Zhang1

  • 1School of Automation, Qingdao University, Qingdao 266071, China.

Sensors (Basel, Switzerland)
|October 29, 2025
PubMed
Summary
This summary is machine-generated.

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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This study introduces a lightweight, self-supervised framework for monocular depth estimation. The novel approach enhances 3D scene understanding in computer vision with improved feature extraction and artifact elimination.

Area of Science:

  • Computer Vision
  • 3D Scene Understanding

Background:

  • Monocular depth estimation is crucial for 3D scene understanding.
  • Applications include autonomous driving and augmented reality.

Purpose of the Study:

  • Propose a lightweight, self-supervised framework for monocular depth estimation.
  • Improve multi-scale feature extraction and artifact elimination.

Main Methods:

  • Introduced a Cross-Dimensional Multi-scale Feature Extraction (CDMs) module.
  • Developed a Sobel Edge Perception-Guided Filtering (SEGF) module using the Sobel operator.
  • Combined parallel multi-scale convolution with sequential feature convolutions.

Main Results:

  • The proposed MsGf framework achieved state-of-the-art performance on KITTI and Make3D datasets.
Keywords:
edge feature extractionguided filteringlightweight modelmulti-scale feature extractionself-supervised monocular depth estimation

Related Experiment Videos

Last Updated: Jan 13, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.0K
  • Demonstrated superior performance with only 0.8 M parameters.
  • Effectively suppressed artifacts and captured structural/edge features.

    • Conclusions:

    • The MsGf framework offers an efficient and effective solution for monocular depth estimation.
    • The novel modules significantly enhance feature extraction and artifact suppression.
    • Achieved competitive results with a significantly reduced parameter count.