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Superb Monocular Depth Estimation Based on Transfer Learning and Surface Normal Guidance.

Kang Huang1, Xingtian Qu1, Shouqian Chen2

  • 1Department of Mechanical Engineering and Automation, School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China.

Sensors (Basel, Switzerland)
|September 2, 2020
PubMed
Summary

This study introduces a novel monocular depth estimation method using a lightweight Convolutional Neural Network (CNN) for drones and robots. The approach refines depth prediction with surface normal guidance, improving 3D scene sensing for navigation.

Keywords:
SFMSLAMmonocular depth estimationmulti-task learningsupervised deep learningsurface normal estimationtransfer learning

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Area of Science:

  • Computer Vision
  • Robotics
  • Artificial Intelligence

Background:

  • Accurate 3D scene perception is crucial for autonomous systems like drones and robots to perform path planning and navigation.
  • Existing monocular depth estimation methods often require complex models or extensive parameters, limiting their efficiency.

Purpose of the Study:

  • To propose a novel and efficient monocular depth estimation method for improved 3D scene sensing.
  • To enhance the accuracy of depth prediction by integrating surface normal guidance.
  • To develop a framework suitable for integration into monocular simultaneous localization and mapping (SLAM) systems.

Main Methods:

  • A lightweight Convolutional Neural Network (CNN) architecture was employed for initial coarse depth prediction.
  • A two-stream encoder-decoder network was utilized for surface normal estimation, hierarchically merging RGB-D images.
  • The coarse depth prediction was refined using the estimated surface normals for improved geometric boundary detail.

Main Results:

  • The proposed method achieved superior detailed depth maps compared to existing state-of-the-art approaches.
  • The framework demonstrated efficiency due to fewer network parameters and a simpler learning structure.
  • Reconstructed 3D point cloud maps validated the effectiveness of the depth prediction.

Conclusions:

  • The novel monocular depth estimation method offers a more efficient and accurate solution for 3D scene understanding.
  • The integration of surface normal guidance significantly enhances the quality of predicted depth maps.
  • The proposed framework shows promise for seamless integration into monocular SLAM systems, advancing robotic navigation capabilities.