Abstract
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.
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Acknowledgments
This work was supported by the DFG projects KL 1142/11-1 (DFG Research Unit FOR 2535 Anticipating Human Behavior) and KL 1142/9-2 (DFG Research Unit FOR 1505 Mapping on Demand).
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Ochmann, S., Klein, R. (2019). Automatic Normal Orientation in Point Clouds of Building Interiors. In: Gavrilova, M., Chang, J., Thalmann, N., Hitzer, E., Ishikawa, H. (eds) Advances in Computer Graphics. CGI 2019. Lecture Notes in Computer Science(), vol 11542. Springer, Cham. https://doi.org/10.1007/978-3-030-22514-8_55
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DOI: https://doi.org/10.1007/978-3-030-22514-8_55
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