Disparity Estimation Using Convolutional Neural Networks with Multi-scale Correlation

Samer Jammal*, Tammam Tillo, Jimin Xiao

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)


Disparity estimation is a long-standing task in computer vision and multiple approaches have been proposed to solve this problem. A recent work based on convolutional neural networks, which uses a correlation layer to perform the matching process, has achieved state-of-the-art results for the disparity estimation task. This correlation layer employs a single kernel unit which is not suitable for low texture content and repeated patterns. In this paper we tackle this problem by using a multi-scale correlation layer with several correlation kernels and different scales. The major target is to integrate the information of the local matching process by combining the benefits of using both a small correlating scale for fine details and bigger scales for larger areas. Furthermore, we investigate the training approach using horizontally elongated patches that fits the disparity estimation task. The results obtained demonstrate the benefits of the proposed approach on both synthetic and real images.

Original languageEnglish
Title of host publicationNeural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
EditorsDerong Liu, Shengli Xie, El-Sayed M. El-Alfy, Dongbin Zhao, Yuanqing Li
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319700892
Publication statusPublished - 2017
Event24th International Conference on Neural Information Processing, ICONIP 2017 - Guangzhou, China
Duration: 14 Nov 201718 Nov 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10636 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference24th International Conference on Neural Information Processing, ICONIP 2017


  • Convolutional neural networks
  • Depth estimation
  • Disparity estimation
  • Multi-scale correlation
  • Stereo vision

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