  We present a novel method for reconstructing parametric, volumetric,
multi-story building models from unstructured, unfiltered indoor point clouds
by means of solving an integer linear optimization problem. Our approach
overcomes limitations of previous methods in several ways: First, we drop
assumptions about the input data such as the availability of separate scans as
an initial room segmentation. Instead, a fully automatic room segmentation and
outlier removal is performed on the unstructured point clouds. Second,
restricting the solution space of our optimization approach to arrangements of
volumetric wall entities representing the structure of a building enforces a
consistent model of volumetric, interconnected walls fitted to the observed
data instead of unconnected, paper-thin surfaces. Third, we formulate the
optimization as an integer linear programming problem which allows for an exact
solution instead of the approximations achieved with most previous techniques.
Lastly, our optimization approach is designed to incorporate hard constraints
which were difficult or even impossible to integrate before. We evaluate and
demonstrate the capabilities of our proposed approach on a variety of complex
real-world point clouds.
