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EdgeStereo: An Effective Multi-task Learning Network for Stereo Matching and Edge Detection

International journal of computer vision, 2020-04, Vol.128 (4), p.910-930 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2020 ;COPYRIGHT 2020 Springer ;Springer Science+Business Media, LLC, part of Springer Nature 2020. ;ISSN: 0920-5691 ;EISSN: 1573-1405 ;DOI: 10.1007/s11263-019-01287-w

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  • Title:
    EdgeStereo: An Effective Multi-task Learning Network for Stereo Matching and Edge Detection
  • Author: Song, Xiao ; Zhao, Xu ; Fang, Liangji ; Hu, Hanwen ; Yu, Yizhou
  • Subjects: Artificial Intelligence ; Artificial neural networks ; Computer Imaging ; Computer Science ; Convolution ; Edge detection ; Image Processing and Computer Vision ; Learning ; Matching ; Modules ; Neural networks ; Pattern Recognition ; Pattern Recognition and Graphics ; Regularization ; Smoothness ; Vision
  • Is Part Of: International journal of computer vision, 2020-04, Vol.128 (4), p.910-930
  • Description: Recently, leveraging on the development of end-to-end convolutional neural networks, deep stereo matching networks have achieved remarkable performance far exceeding traditional approaches. However, state-of-the-art stereo frameworks still have difficulties at finding correct correspondences in texture-less regions, detailed structures, small objects and near boundaries, which could be alleviated by geometric clues such as edge contours and corresponding constraints. To improve the quality of disparity estimates in these challenging areas, we propose an effective multi-task learning network, EdgeStereo , composed of a disparity estimation branch and an edge detection branch, which enables end-to-end predictions of both disparity map and edge map. To effectively incorporate edge cues, we propose the edge-aware smoothness loss and edge feature embedding for inter-task interactions. It is demonstrated that based on our unified model, edge detection task and stereo matching task can promote each other. In addition, we design a compact module called residual pyramid to replace the commonly-used multi-stage cascaded structures or 3-D convolution based regularization modules in current stereo matching networks. By the time of the paper submission, EdgeStereo achieves state-of-art performance on the FlyingThings3D dataset, KITTI 2012 and KITTI 2015 stereo benchmarks, outperforming other published stereo matching methods by a noteworthy margin. EdgeStereo also achieves comparable generalization performance for disparity estimation because of the incorporation of edge cues.
  • Publisher: New York: Springer US
  • Language: English
  • Identifier: ISSN: 0920-5691
    EISSN: 1573-1405
    DOI: 10.1007/s11263-019-01287-w
  • Source: AUTh Library subscriptions: ProQuest Central
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