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DCPNet: A Densely Connected Pyramid Network for Monocular Depth Estimation.

Zhitong Lai1,2, Rui Tian1, Zhiguo Wu1

  • 1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China.

Sensors (Basel, Switzerland)
|October 26, 2021
PubMed
Summary
This summary is machine-generated.

DCPNet enhances deep monocular depth estimation by densely connecting pyramid network stages for improved multi-scale feature fusion. This novel approach achieves state-of-the-art results on benchmark datasets.

Keywords:
dense connectionfeature fusionmonocular depth estimationpyramid networks

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Area of Science:

  • Computer Vision
  • Deep Learning
  • Artificial Intelligence

Background:

  • Pyramid networks are effective for fusing multi-scale features in deep monocular depth estimation.
  • Current methods often limit feature fusion to adjacent stages within the pyramid structure.

Purpose of the Study:

  • To introduce DCPNet, a densely connected pyramid network for enhanced multi-scale feature fusion.
  • To improve feature fusion by enabling connections between non-adjacent stages in a pyramid network.

Main Methods:

  • DCPNet utilizes a densely connected pyramid architecture inspired by DenseNet.
  • A novel Dense Connection Module (DCM) is designed for efficient feature fusion across multiple scales.
  • A new approach to the common upscale operation is incorporated.

Main Results:

  • DCPNet achieves state-of-the-art performance on both outdoor (KITTI) and indoor (NYU Depth V2) benchmark datasets.
  • The dense connection strategy effectively fuses features from multiple pyramid stages.
  • The proposed DCM and upscale operation contribute to improved depth estimation accuracy.

Conclusions:

  • DCPNet offers a more efficient and effective method for fusing multi-scale features in pyramid-like networks.
  • The densely connected approach significantly advances deep monocular depth estimation.
  • The model demonstrates strong generalization capabilities across diverse datasets.