Automatic normal orientation in point clouds of building interiors

In proceedings of Computer Graphics International Conference, Springer, pages 556-563, 2019
 

Abstract

Orienting surface normals correctly and consistently is a fundamental problem in geometry processing. Applications such as visualization, feature detection, and geometry reconstruction often rely on the availability of correctly oriented normals. Many existing approaches for automatic orientation of normals on meshes or point clouds make severe assumptions on the input data or the topology of the underlying object which are not applicable to real-world measurements of urban scenes. In contrast, our approach is specifically tailored to the challenging case of unstructured indoor point cloud scans of multi-story, multi-room buildings. We evaluate the correctness and speed of our approach on multiple real-world point cloud datasets.

A pre-print version is available on arXiv at: https://arxiv.org/abs/1901.06487

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Bibtex

@INPROCEEDINGS{ochmann2019automatic,
        author = {Ochmann, Sebastian and Klein, Reinhard},
         pages = {556--563},
         title = {Automatic normal orientation in point clouds of building interiors},
     booktitle = {Computer Graphics International Conference},
          year = {2019},
  organization = {Springer},
      abstract = {Orienting surface normals correctly and consistently is a fundamental problem in geometry
                  processing. Applications such as visualization, feature detection, and geometry reconstruction often
                  rely on the availability of correctly oriented normals. Many existing approaches for automatic
                  orientation of normals on meshes or point clouds make severe assumptions on the input data or the
                  topology of the underlying object which are not applicable to real-world measurements of urban
                  scenes. In contrast, our approach is specifically tailored to the challenging case of unstructured
                  indoor point cloud scans of multi-story, multi-room buildings. We evaluate the correctness and speed
                  of our approach on multiple real-world point cloud datasets.}
}