Volume XLII-3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2565-2570, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2565-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3, 2565-2570, 2018
https://doi.org/10.5194/isprs-archives-XLII-3-2565-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  02 May 2018

02 May 2018

STEREO IMAGE DENSE MATCHING BY INTEGRATING SIFT AND SGM ALGORITHM

Y. Zhou1, Y. Song1, and J. Lu2 Y. Zhou et al.
  • 1Faculty of Information Engineering, China University of Geosciences, Wuhan, China
  • 2China Railway Engineering Consulting Group Co., Ltd

Keywords: Stereo Matching, Semi-Global Matching, SIFT, Dense Matching, Disparity Estimation, Census

Abstract. Semi-global matching(SGM) performs the dynamic programming by treating the different path directions equally. It does not consider the impact of different path directions on cost aggregation, and with the expansion of the disparity search range, the accuracy and efficiency of the algorithm drastically decrease. This paper presents a dense matching algorithm by integrating SIFT and SGM. It takes the successful matching pairs matched by SIFT as control points to direct the path in dynamic programming with truncating error propagation. Besides, matching accuracy can be improved by using the gradient direction of the detected feature points to modify the weights of the paths in different directions. The experimental results based on Middlebury stereo data sets and CE-3 lunar data sets demonstrate that the proposed algorithm can effectively cut off the error propagation, reduce disparity search range and improve matching accuracy.