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Confidence Map Based 3D Cost Aggregation with Multiple Minimum Spanning Trees for Stereo Matching

  • Yuhao Xiao
  • Dingding Xu
  • Guijin WangEmail author
  • Xiaowei Hu
  • Yongbing Zhang
  • Xiangyang Ji
  • Li Zhang
Conference paper
  • 131 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12046)

Abstract

Stereo matching is a challenging problem due to the mismatches caused by difficult environment conditions. In this paper, we propose an enhanced version of our previous work, denoted as 3DMST-CM, to handle challenging cases and obtain a high-accuracy disparity map based on the ambiguity of image pixels. We develop a module of distinctiveness analysis to classify pixels into distinctive and ambiguous pixels. Then distinctive pixels are utilized as anchor pixels to help match ambiguous pixels accurately. The experimental results demonstrate the effectiveness of our method and reach state-of-the-art on the Middlebury 3.0 benchmark.

Keywords

Stereo matching Distinctiveness analysis 3D label 

References

  1. 1.
    Scharstein, D., Szeliski, R.H.H.: Middlebury stereo evaluation. Version 3. http://vision.middlebury.edu/stereo/eval3/
  2. 2.
    Drouyer, S., Beucher, S., Bilodeau, M., Moreaud, M., Sorbier, L.: Sparse stereo disparity map densification using hierarchical image segmentation. In: Angulo, J., Velasco-Forero, S., Meyer, F. (eds.) ISMM 2017. LNCS, vol. 10225, pp. 172–184. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-57240-6_14CrossRefzbMATHGoogle Scholar
  3. 3.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Efficient graph-based image segmentation. Int. J. Comput. Vis. 59(2), 167–181 (2004)CrossRefGoogle Scholar
  4. 4.
    Kim, K.R., Kim, C.S.: Adaptive smoothness constraints for efficient stereo matching using texture and edge information. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3429–3433. IEEE (2016)Google Scholar
  5. 5.
    Li, L., Yu, X., Zhang, S., Zhao, X., Zhang, L.: 3D cost aggregation with multiple minimum spanning trees for stereo matching. Appl. Opt. 56(12), 3411–3420 (2017)CrossRefGoogle Scholar
  6. 6.
    Li, L., Zhang, S., Yu, X., Zhang, L.: PMSC: PatchMatch-based superpixel cut for accurate stereo matching. IEEE Trans. Circuits Syst. Video Technol. 28(3), 679–692 (2016)CrossRefGoogle Scholar
  7. 7.
    Mao, W., Wang, M., Zhou, J., Gong, M.: Semi-dense stereo matching using dual CNNs. In: IEEE Winter Conference on Applications of Computer Vision, pp. 1588–1597 (2019).  https://doi.org/10.1109/WACV.2019.00174
  8. 8.
    Mozerov, M.G., van de Weijer, J.: One-view occlusion detection for stereo matching with a fully connected CRF model. IEEE Trans. Image Process. 28(6), 2936–2947 (2019)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Park, H., Lee, K.M.: Look wider to match image patches with convolutional neural networks. IEEE Signal Process. Lett. 24(12), 1788–1792 (2016)CrossRefGoogle Scholar
  10. 10.
    Scharstein, D., et al.: High-resolution stereo datasets with subpixel-accurate ground truth. In: Jiang, X., Hornegger, J., Koch, R. (eds.) GCPR 2014. LNCS, vol. 8753, pp. 31–42. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-11752-2_3CrossRefGoogle Scholar
  11. 11.
    Shi, C., Wang, G., Pei, X., He, B., Lin, X.: Stereo matching using local plane fitting in confidence-based support window. IEICE Trans. Inf. Syst. 95(2), 699–702 (2012)CrossRefGoogle Scholar
  12. 12.
    Shi, C., Wang, G., Yin, X., Pei, X., He, B., Lin, X.: High-accuracy stereo matching based on adaptive ground control points. IEEE Trans. Image Process. 24(4), 1412–1423 (2015)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Taniai, T., Matsushita, Y., Sato, Y., Naemura, T.: Continuous 3D label stereo matching using local expansion moves. IEEE Trans. Pattern Anal. Mach. Intell. 40(11), 2725–2739 (2018)CrossRefGoogle Scholar
  14. 14.
    Ye, X., Li, J., Wang, H., Huang, H., Zhang, X.: Efficient stereo matching leveraging deep local and context information. IEEE Access 5, 18745–18755 (2017)CrossRefGoogle Scholar
  15. 15.
    Zbontar, J., LeCun, Y.: Computing the stereo matching cost with a convolutional neural network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1592–1599 (2015)Google Scholar
  16. 16.
    Zhang, C., Li, Z., Cheng, Y., Cai, R., Chao, H., Rui, Y.: MeshStereo: a global stereo model with mesh alignment regularization for view interpolation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2057–2065 (2015)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Yuhao Xiao
    • 1
  • Dingding Xu
    • 2
  • Guijin Wang
    • 1
    Email author
  • Xiaowei Hu
    • 1
  • Yongbing Zhang
    • 2
  • Xiangyang Ji
    • 3
  • Li Zhang
    • 1
  1. 1.Department of Electronic EngineeringTsinghua UniversityBeijingChina
  2. 2.Graduate School at ShenzhenTsinghua UniversityShenzhenChina
  3. 3.Department of AutomationTsinghua UniversityBeijingChina

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