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SFA-MDEN: Semantic-Feature-Aided Monocular Depth Estimation Network Using Dual Branches.

Rui Wang1, Jialing Zou1, James Zhiqing Wen2

  • 1Digital Photoelectric Information Processing Technology Laboratory, School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China.

Sensors (Basel, Switzerland)
|August 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new Semantic-Feature-Aided Monocular Depth Estimation Network (SFA-MDEN) for unsupervised depth prediction. The SFA-MDEN effectively uses semantic information to improve monocular depth estimation accuracy.

Keywords:
feature fusionmonocular depth estimationmulti-task deep learningsemantic segmentation

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Deep Learning

Background:

  • Unsupervised monocular depth estimation is crucial for lightweight vision systems.
  • Semantic information enhances depth estimation, but multi-task learning faces annotation limitations.
  • Existing datasets often lack comprehensive annotations for multi-task learning.

Purpose of the Study:

  • To propose a novel network architecture, SFA-MDEN, for monocular depth estimation.
  • To leverage semantic features to improve depth prediction accuracy without relying on multi-type annotations.
  • To enable training with accessible datasets for depth estimation and semantic segmentation.

Main Methods:

  • Developed the Semantic-Feature-Aided Monocular Depth Estimation Network (SFA-MDEN).
  • Extracted multi-resolution depth and semantic features.
  • Fused semantic and depth feature maps directly for prediction, bypassing traditional loss functions.
  • Utilized two accessible datasets for training.

Main Results:

  • SFA-MDEN demonstrated competitive accuracy in monocular depth estimation.
  • The proposed method showed strong generalization capabilities across different datasets.
  • Feature map fusion proved effective for integrating semantic and depth information.

Conclusions:

  • SFA-MDEN offers an effective approach to monocular depth estimation using semantic information.
  • The method overcomes limitations of multi-task learning by avoiding extensive annotation requirements.
  • SFA-MDEN achieves state-of-the-art performance with improved generalization.