Automatic normal orientation in point clouds of building interiors

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

ABSTRACT Estimating the In/Out Function of a Surface Represented by Points

We present a method to estimate the in/out function of a closed surface represented by an unorganized set of data points. From the in/out function, we compute an approximation of the signed distance function to a surface M whose sampling are given by this set of points. The procedure correctly detects the interior and the exterior of M, evenifM is multiply connected. The representation of the signed distance function combines the advantages of two previously known schemes, “Implicit Simplicial Models ” and “Adaptively Sampled Distance Fields”, incorporating new features deriving from the concept of a Binary Multitriangulation