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Correct and consistent normal orientation is a fundamental problem in geometry processing. Applications such as feature detection and geometry reconstruction often rely on correctly oriented normals. Many existing approaches make severe assumptions on the input data or the topology of the underlying object which are not applicable to 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.

Automatic Normal Orientation in Point Clouds of Building Interiors