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Predicting Depth from Single RGB Images with Pyramidal Three-Streamed Networks.

Songnan Chen1,2, Mengxia Tang3,4, Jiangming Kan5,6

  • 1School of Technology, Beijing Forestry University, No. 35 Qinghua East Road, Haidian District, Beijing 10 0083, China. chensongnan@xyafu.edu.cn.

Sensors (Basel, Switzerland)
|February 10, 2019
PubMed
Summary

This study introduces a new Pyramidal Third-Streamed Network (PTSN) for accurately predicting depth from single RGB images. The novel network enhances feature extraction and employs a unique loss function for improved depth and contour prediction.

Keywords:
monocular imagepredicting depthpyramidalthird-streamed network

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Deep Learning

Background:

  • Monocular depth estimation is a challenging problem due to inherent ill-posedness and ambiguity.
  • Existing methods often struggle with robustness and computational efficiency.

Purpose of the Study:

  • To develop a novel deep learning network for accurate monocular depth estimation.
  • To improve the robustness and reduce the computational complexity of depth prediction models.

Main Methods:

  • Proposed a Pyramidal Third-Streamed Network (PTSN) utilizing multiresolution features.
  • Replaced fully connected layers with fully convolutional layers and introduced an upconvolution structure.
  • Developed a new loss function incorporating scale-invariant, horizontal, and vertical gradient losses.

Main Results:

  • PTSN demonstrated improved accuracy in depth prediction compared to existing methods on the NYU Depth v2 dataset.
  • The network effectively extracts multiresolution features for enhanced robustness.
  • The proposed loss function aids in predicting accurate depth values and local contours.

Conclusions:

  • The Pyramidal Third-Streamed Network (PTSN) offers a robust and efficient solution for monocular depth estimation.
  • The novel network architecture and loss function contribute to state-of-the-art performance in depth prediction.