Efficient Surface-Aware Semi-Global Matching with Multi-View Plane-Sweep Sampling

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

Fast and robust generation of semantic urban terrain models from UAV video streams

We present an algorithm for extracting Level of Detail 2 (LOD2) building models from video streams captured by Unmaned Aerial Vehicles (UAVs). Typically, such imagery is of limited radiometric quality but the surface is captured with large redundancy. The first contribution of this paper is a novel algorithm exploiting this redundancy for precise depth computation. This is realized by fusing consistent depth estimations across single stereo models and generating a 2.5D elevation map from the resulting point clouds. Disparity maps are derived by a coarse-to-fine Semi-Global-Matching (SGM) method performing well on noisy imagery. The second contribution concerns a challenging step of the context-based urban terrain modeling: Dominant planes extraction for building reconstruction. Because of noisy data and complicated roof structures, both dominant plane parameters and initial values for support sets of planes are obtained by the J-Linkage algorithm. An improved pointto-plane labeling is presented to encourage the assignment of proximate points to the same plane. This is accomplished by non-local, Markov Random Field (MRF) - based optimization and segmentation of color information. The potential and the limitations of the proposed methods are shown using an UAV video sequence of limited radiometric quality