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With FaSS-MVS, we present an approach for fast multi-view stereo withsurface-aware Semi-Global Matching that allows for rapid depth and normal mapestimation from monocular aerial video data captured by UAVs. The dataestimated by FaSS-MVS, in turn, facilitates online 3D mapping, meaning that a3D map of the scene is immediately and incrementally generated while the imagedata is acquired or being received. FaSS-MVS is comprised of a hierarchicalprocessing scheme in which depth and normal data, as well as correspondingconfidence scores, are estimated in a coarse-to-fine manner, allowing toefficiently process large scene depths which are inherent to oblique imagerycaptured by low-flying UAVs. The actual depth estimation employs a plane-sweepalgorithm for dense multi-image matching to produce depth hypotheses from whichthe actual depth map is extracted by means of a surface-aware semi-globaloptimization, reducing the fronto-parallel bias of SGM. Given the estimateddepth map, the pixel-wise surface normal information is then computed byreprojecting the depth map into a point cloud and calculating the normalvectors within a confined local neighborhood. In a thorough quantitative andablative study we show that the accuracies of the 3D information calculated byFaSS-MVS is close to that of state-of-the-art approaches for offline multi-viewstereo, with the error not even being one magnitude higher than that of COLMAP.At the same time, however, the average run-time of FaSS-MVS to estimate asingle depth and normal map is less than 14 % of that of COLMAP, allowing toperform an online and incremental processing of Full-HD imagery at 1-2 Hz.

FaSS-MVS -- Fast Multi-View Stereo with Surface-Aware Semi-Global Matching from UAV-borne Monocular Imagery