Disparity Refinement in Depth Discontinuity Using Robustly Matched Straight Lines for Digital Surface Model Generation

Rongjun Qin, Min Chen, Xu Huang, Kun Hu

Abstract: Dense image matching using remote sensing images is of particular importance for providing ground geometric data for object modeling and analysis. These methods normally operate in a rectified (epipolar) space producing disparity images that are correlated with the final three-dimensional geometry. However, the resulting digital surface models can be problematic on discontinuities of the terrain object, e.g., building edges, which largely limit their practical use for highly accurate object modeling. Existing works put forth efforts for dealing with this issue by defining better edge constraints and more global energy optimization, while intended to improve the disparity maps, it might be challenging to leverage the result of all the pixels through a single energy minimization, leading to either oversmoothed object boundaries or noisy surfaces. In this paper, we propose an intuitive method that integrates straight line primitives to enhance the disparity map. For each matched line, we perform a local discontinuity analysis and propose an intensity-based weighting method for a local plane fitting using iteratively solved weighted least squares adjustment, such that straightness of the object's edges (e.g., buildings) can be preserved, as the straight lines are detected and matched. Experiments on both aerial and satellite dataset show that the proposed method yields visually clear object edges and has achieved higher accuracy (2-3 pixels improvement) around edges than DSM generated from typical stereo matching result (i.e., from semiglobal matching).

External IDs:dblp:journals/staeors/QinCHH19