Dense Stereo Matching from Separated Views of Wide-Baseline Images

  • Qian Zhang
  • King Ngi Ngan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6474)


In this paper, we present a dense stereo matching algorithm from multiple wide-baseline images with separated views. The algorithm utilizes the coarse-to-fine strategy to propagate the sparse feature matching to dense stereo for image pixels. First, the images are segmented into non-overlapping homogeneous partitions. Then, in the coarse step, the initial disparity map is estimated by assigning the sparse feature correspondences, where the spatial location of these features is incorporated with the over-segmentation. The initial occlusion status is obtained by cross-checking test. Finally, the stereo maps are refined by the proposed discontinuity-preserving regularization algorithm, which directly coupling the disparity and occlusion labeling. The experimental results implemented on the real date sets of challenging samples, including the wide-baseline image pairs with both identical scale and different scale, demonstrated the good subjective performance of the proposed method.


color segmentation sparse matching stereo estimation wide-baseline images 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Qian Zhang
    • 1
  • King Ngi Ngan
    • 1
  1. 1.Department of Electronic EngineeringThe Chinese University of Hong KongShatinHong Kong

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