Multiscale Feature Extractors for Stereo Matching Cost Computation

Kyung Rae Kim, Yeong Jun Koh, Chang-Su Kim

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


We propose four efficient feature extractors based on convolutional neural networks for stereo matching cost computation. Two of them generate multiscale features with diverse receptive field sizes. These multiscale features are used to compute the corresponding multiscale matching costs. We then determine an optimal cost by combining the multiscale costs using edge information. On the other hand, the other two feature extractors produce uni-scale features by combining multiscale features directly through fully connected layers. Finally, after obtaining matching costs using one of the four extractors, we determine optimal disparities based on the cross-based cost aggregation and the semiglobal matching. Extensive experiments on the Middlebury stereo data sets demonstrate the effectiveness and efficiency of the proposed algorithm. Specifically, the proposed algorithm provides competitive matching performance with the state of the arts, while demanding lower computational complexity.

Original languageEnglish
Pages (from-to)27971-27983
Number of pages13
JournalIEEE Access
Publication statusPublished - 2018 May 17


  • Stereo matching
  • convolutional neural networks
  • matching cost computation
  • multiscale feature extraction

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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