A statistical approach for extraction of feature lines from point clouds

• Presence of a statistical approach based on Poisson distribution computing different thresholds for different local features.
• Goes beyond user-setting feature measure thresholds of previous feature extraction methods.
• Enables line reconstruction based on geometry of feature points and link information.

This paper firstly introduces the use of a statistical model based on the Poisson distribution as a tool for extracting feature points from point clouds. When the features on a model are non-uniform or the surfaces are not completely smooth, the thresholds for different local features must be set differently. Such case is ill-suited to all previous global fixed thresholds dependent methods. The Poisson distribution based method is preferable because it computes different thresholds for different local features adaptively depending on the natural properties of the surfaces. Secondly, the region information analysis is employed to cluster the border/edge points, identify the corner points and acquire the link information for feature lines. Finally, the complete feature lines are reconstructed based on the geometry representations of the border point clusters that are created by adapting L1-median locally, instead of using distance/path parameters. The proposed method does not need any prior surface reconstruction and is not largely affected by noisy points, neighborhood scales or sampling quality. We demonstrate the benefits of our method with the favorable results for real-world point clouds with varying types of features and the application of the generated feature lines to the computer aided line-drawings generation of Terracotta.

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