Volume IV-1
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 77-84, 2018
https://doi.org/10.5194/isprs-annals-IV-1-77-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-1, 77-84, 2018
https://doi.org/10.5194/isprs-annals-IV-1-77-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  26 Sep 2018

26 Sep 2018

DISPARITY REFINEMENT OF BUILDING EDGES USING ROBUSTLY MATCHED STRAIGHT LINES FOR STEREO MATCHING

X. Huang1, R. Qin1,2, and M. Chen3 X. Huang et al.
  • 1Department of Civil, Environmental and Geodetic Engineering, The Ohio State University, 218B Bolz Hall, 2036 Neil Avenue, Columbus, OH 43210, USA
  • 2Department of Electrical and Computer Engineering, The Ohio State University, 205 Dreese Labs, 2015 Neil Avenue, Columbus, OH 43210, USA
  • 3Lyles School of Civil Engineering, Purdue University, West Lafayette, 47907, USA

Keywords: Matched Straight Lines, Disparity Refinement, Support Window Definition, Edge Detection, Plane-based Adjustment

Abstract. Stereo dense matching has already been one of the dominant tools in 3D reconstruction of urban regions, due to its low cost and high flexibility in generating 3D points. However, the image-derived 3D points are often inaccurate around building edges, which limit its use in several vision tasks (e.g. building modelling). To generate 3D point clouds or digital surface models (DSM) with sharp boundaries, this paper integrates robustly matched lines for improving dense matching, and proposes a non-local disparity refinement of building edges through an iterative least squares plane adjustment approach. In our method, we first extract and match straight lines in images using epipolar constraints, then detect building edges from these straight lines by comparing matching results on both sides of straight lines, and finally we develop a non-local disparity refinement method through an iterative least squares plane adjustment constrained by matched straight lines to yield sharper and more accurate edges. Experiments conducted on both satellite and aerial data demonstrate that our proposed method is able to generate more accurate DSM with sharper object boundaries.