ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume IV-2/W7
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W7, 137–144, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W7-137-2019
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., IV-2/W7, 137–144, 2019
https://doi.org/10.5194/isprs-annals-IV-2-W7-137-2019

  16 Sep 2019

16 Sep 2019

EFFICIENT SURFACE-AWARE SEMI-GLOBAL MATCHING WITH MULTI-VIEW PLANE-SWEEP SAMPLING

B. Ruf1,2, T. Pollok1, and M. Weinmann2 B. Ruf et al.
  • 1Fraunhofer IOSB, Video Exploitation Systems, Karlsruhe, Germany
  • 2Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, Karlsruhe, Germany

Keywords: Depth Estimation, Normal Map Estimation, Semi-Global-Matching, Multi-View, Plane-Sweep Stereo, Online Processing, Oblique Aerial Imagery

Abstract. Online augmentation of an oblique aerial image sequence with structural information is an essential aspect in the process of 3D scene interpretation and analysis. One key aspect in this is the efficient dense image matching and depth estimation. Here, the Semi-Global Matching (SGM) approach has proven to be one of the most widely used algorithms for efficient depth estimation, providing a good trade-off between accuracy and computational complexity. However, SGM only models a first-order smoothness assumption, thus favoring fronto-parallel surfaces. In this work, we present a hierarchical algorithm that allows for efficient depth and normal map estimation together with confidence measures for each estimate. Our algorithm relies on a plane-sweep multi-image matching followed by an extended SGM optimization that allows to incorporate local surface orientations, thus achieving more consistent and accurate estimates in areasmade up of slanted surfaces, inherent to oblique aerial imagery. We evaluate numerous configurations of our algorithm on two different datasets using an absolute and relative accuracy measure. In our evaluation, we show that the results of our approach are comparable to the ones achieved by refined Structure-from-Motion (SfM) pipelines, such as COLMAP, which are designed for offline processing. In contrast, however, our approach only considers a confined image bundle of an input sequence, thus allowing to perform an online and incremental computation at 1Hz–2Hz.