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Review of Stereo Matching Algorithms Based on Deep Learning.

Kun Zhou1,2, Xiangxi Meng3, Bo Cheng2,4

  • 1School of Mathematics Science, Peking University, Beijing, China.

Computational Intelligence and Neuroscience
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Summary

Deep learning significantly enhances stereo matching algorithms, outperforming traditional methods. This review categorizes deep learning approaches into non-end-to-end, end-to-end, and unsupervised methods for stereo vision.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Stereo vision is a rapidly advancing research area.
  • Deep learning has revolutionized stereo matching, surpassing conventional techniques.
  • Recent progress highlights the potential of deep learning in stereo vision.

Purpose of the Study:

  • To provide a comprehensive overview of deep learning-based stereo matching algorithms.
  • To classify these algorithms into distinct categories for clarity.
  • To analyze the strengths, weaknesses, and challenges of each category.

Main Methods:

  • Categorization of algorithms into non-end-to-end, end-to-end, and unsupervised learning.
  • Review of prominent deep learning approaches within each category.
  • Comparative analysis based on speed, accuracy, and computational time.

Main Results:

  • Deep learning algorithms demonstrate superior performance in stereo matching.
  • Each category (non-end-to-end, end-to-end, unsupervised) offers unique advantages and disadvantages.
  • Key challenges and areas for future research are identified.

Conclusions:

  • Deep learning is pivotal for state-of-the-art stereo matching.
  • Understanding algorithm categories aids in selecting appropriate methods.
  • Further research is needed to address existing limitations and enhance performance.