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Structural Similarity Measurement Based Cost Function for Stereo Matching of Automotive Applications.

Oussama Zeglazi1, Mohammed Rziza1, Aouatif Amine2

  • 1LRIT, Rabat IT Center, Faculty of Sciences, Mohammed V University, Rabat B.P. 1014, Morocco.

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|August 30, 2021
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Summary

This study introduces structural information similarity metrics (SSIM and GSSIM) for stereo matching algorithms. These metrics improve initial cost values and enhance performance under radiometric changes, crucial for real-world scenes.

Keywords:
KITTI 2012KITTI 2015cross-based aggregation methodstereo matchingstructure similarity measurement

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Area of Science:

  • Computer Vision
  • Image Processing
  • Artificial Intelligence

Background:

  • Human visual perception relies on structural information for stereo correspondence.
  • Efficient stereo matching algorithms require robust structural information.
  • Radiometric changes pose a significant challenge in real-world stereo matching.

Purpose of the Study:

  • To develop and evaluate novel cost functions for stereo matching algorithms.
  • To incorporate structural information similarity (SSIM and GSSIM) for improved patch representation.
  • To assess the performance of these cost functions under radiometric variations.

Main Methods:

  • Extraction of structural information similarity from image intensity (SSIM) and gradients (GSSIM).
  • Development of new cost functions based on patch structure similarity.
  • Evaluation of cost functions in two stages: without and with fast adaptive aggregation.
  • Testing on KITTI 2012 and KITTI 2015 road traffic scene benchmarks.

Main Results:

  • Proposed structural similarity measurements (SSIM, GSSIM) provide reliable initial cost values.
  • The new cost functions demonstrate improved performance, particularly under radiometric changes.
  • The evaluation confirms the effectiveness of the proposed methods on challenging real-world datasets.

Conclusions:

  • Incorporating structural information similarity significantly enhances stereo matching accuracy.
  • The proposed SSIM and GSSIM metrics offer a robust solution for stereo matching in scenes with radiometric variations.
  • This work contributes to more reliable and efficient stereo vision systems.