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Discrete plane segmentation and estimation from a point cloud using local geometric patterns

Abstract : This paper presents a method for segmenting a 3D point cloud into planar surfaces using recently obtained discrete geometry results. In discrete geometry, a discrete plane is defined as a set of grid points lying between two parallel planes with a small distance, called thickness. Contrarily to the continuous case, there exist a finite number of local geometric patterns (LGPs) appearing on discrete planes. Moreover, such a LGP does not possess the unique normal vector but a set of normal vectors. By using those LGP properties, we first reject non-linear points from a point cloud, and then classify non-rejected points whose LGPs can have common normal vectors into a planar-surface-point set. From each segmented point set, we also estimate parameters of a discrete plane by minimizing its thickness.
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Yukiko Kenmochi, Lilian Buzer, Akihiro Sugimoto, Ikuko Shimizu. Discrete plane segmentation and estimation from a point cloud using local geometric patterns. International Journal of Automation and Computing, Springer Verlag, 2008, 5 (3), pp.246-256. ⟨10.1007/s11633-008-0246-1⟩. ⟨hal-00622344⟩

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