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In this paper we present a scalable approach for robustly computing a 3Dsurface mesh from multi-scale multi-view stereo point clouds that can handleextreme jumps of point density (in our experiments three orders of magnitude).The backbone of our approach is a combination of octree data partitioning,local Delaunay tetrahedralization and graph cut optimization. Graph cutoptimization is used twice, once to extract surface hypotheses from localDelaunay tetrahedralizations and once to merge overlapping surface hypotheseseven when the local tetrahedralizations do not share the same topology.Thisformulation allows us to obtain a constant memory consumption per sub-problemwhile at the same time retaining the density independent interpolationproperties of the Delaunay-based optimization. On multiple public datasets, wedemonstrate that our approach is highly competitive with the state-of-the-artin terms of accuracy, completeness and outlier resilience. Further, wedemonstrate the multi-scale potential of our approach by processing a newlyrecorded dataset with 2 billion points and a point density variation of morethan four orders of magnitude - requiring less than 9GB of RAM per process.

Scalable Surface Reconstruction from Point Clouds with Extreme Scale and Density Diversity