A Fast and Accurate Plane Detection Algorithm for Large Noisy Point Clouds Using Filtered Normals and Voxel Growing

Abstract : With the improvement of 3D scanners, we produce point clouds with more and more points often exceeding millions of points. Then we need a fast and accurate plane detection algorithm to reduce data size. In this article, we present a fast and accurate algorithm to detect planes in unorganized point clouds using filtered normals and voxel growing. Our work is based on a first step in estimating better normals at the data points, even in the presence of noise. In a second step, we compute a score of local plane in each point. Then, we select the best local seed plane and in a third step start a fast and robust region growing by voxels we call voxel growing. We have evaluated and tested our algorithm on different kinds of point cloud and compared its performance to other algorithms.
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https://hal-mines-paristech.archives-ouvertes.fr/hal-01097361
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3DPVT_2010.pdf
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  • HAL Id : hal-01097361, version 1

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Jean-Emmanuel Deschaud, François Goulette. A Fast and Accurate Plane Detection Algorithm for Large Noisy Point Clouds Using Filtered Normals and Voxel Growing. 3DPVT, May 2010, Paris, France. ⟨hal-01097361⟩

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