In this paper we present a scalable approach for robustly computing a 3D
surface mesh from multi-scale multi-view stereo point clouds that can handle
extreme 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 cut
optimization is used twice, once to extract surface hypotheses from local
Delaunay tetrahedralizations and once to merge overlapping surface hypotheses
even when the local tetrahedralizations do not share the same topology.This
formulation allows us to obtain a constant memory consumption per sub-problem
while at the same time retaining the density independent interpolation
properties of the Delaunay-based optimization. On multiple public datasets, we
demonstrate that our approach is highly competitive with the state-of-the-art
in terms of accuracy, completeness and outlier resilience. Further, we
demonstrate the multi-scale potential of our approach by processing a newly
recorded dataset with 2 billion points and a point density variation of more
than four orders of magnitude - requiring less than 9GB of RAM per process.

本文提出一种可扩展且健壮的方法，通过多视点三维立体点云计算三维表面网格，能够处理点密度极大跳跃，用于提取表面假设和合并表面假设，具有卓越的精度和完整性。