Automatic reconstruction of fully volumetric 3D building models from oriented point clouds

In: ISPRS Journal of Photogrammetry and Remote Sensing (Mai 2019), 151(251-262)
 

Abstract

We present a novel method for reconstructing parametric, volumetric, multi-story building models from unstructured, unfiltered indoor point clouds with oriented normals 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.

A pre-print version is available on arXiv at: https://arxiv.org/abs/1907.00631

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Bibtex

@ARTICLE{Ochmann-2019-Reconstruction,
    author = {Ochmann, Sebastian and Vock, Richard and Klein, Reinhard},
     pages = {251--262},
     title = {Automatic reconstruction of fully volumetric 3D building models from oriented point clouds},
   journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
    volume = {151},
      year = {2019},
     month = may,
  abstract = {We present a novel method for reconstructing parametric, volumetric, multi-story building models
              from unstructured, unfiltered indoor point clouds with oriented normals 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.},
       url = {https://www.sciencedirect.com/science/article/abs/pii/S0924271619300863},
       doi = {10.1016/j.isprsjprs.2019.03.017}
}