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- W2070575087 abstract "This paper presents a new method for point cloud simplification. The method searches for a subset of the original point cloud data such that the maximum geometric deviation between the original and simplified data sets is below a specified error bound. The underlying principle of the simplification process is to partition the original data set into piecewise point clusters and represent each cluster by a single point. By iteratively updating the partition and efficiently evaluating the resulting geometric deviations, the proposed method is able to yield a simplified point cloud that satisfies the error bound constraint and contains near minimum number of data points." @default.
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- W2070575087 date "2007-01-01" @default.
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- W2070575087 title "Point-cloud simplification with bounded geometric deviations" @default.
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- W2070575087 doi "https://doi.org/10.1504/ijcat.2007.017235" @default.
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