A fast line segment based dense stereo algorithm using tree dynamic programming

  • Authors:

  • Yi Deng;Xueyin Lin

  • Affiliations:

  • Department of Computer Science, Institute of HCI and Media Integration, Key Lab of Pervasive Computing(MOE), Tsinghua University, Beijing, P.R. China;Department of Computer Science, Institute of HCI and Media Integration, Key Lab of Pervasive Computing(MOE), Tsinghua University, Beijing, P.R. China

  • Venue:
  • ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part III
  • Year:
  • 2006

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Abstract

Many traditional stereo correspondence methods emphasized on utilizing epipolar constraint and ignored the information embedded in inter-epipolar lines. Actually some researchers have already proposed several grid-based algorithms for fully utilizing information embodied in both intra- and inter-epipolar lines. Though their performances are greatly improved, they are very time-consuming. The new graph-cut and believe-propagation methods have made the grid-based algorithms more efficient, but time-consuming still remains a hard problem for many applications. Recently, a tree dynamic programming algorithm is proposed. Though the computation speed is much higher than that of grid-based methods, the performance is degraded apparently. We think that the problem stems from the pixel-based tree construction. Many edges in the original grid are forced to be cut out, and much information embedded in these edges is thus lost. In this paper, a novel line segment based stereo correspondence algorithm using tree dynamic programming (LSTDP) is presented. Each epipolar line of the reference image is segmented into segments first, and a tree is then constructed with these line segments as its vertexes. The tree dynamic programming is adopted to compute the correspondence of each line segment. By using line segments as the vertexes instead of pixels, the connection between neighboring pixels within the same region can be reserved as completely as possible. Experimental results show that our algorithm can obtain comparable performance with state-of-the-art algorithms but is much more time-efficient.