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A Multilevel Feature Points Detecting Method upon Point-Octree for Point-Based Models
Abstract:
In this paper, we present a multilevel feature points extracting and displaying method for point-based models to improve the fast detecting of the multilevel feature of large-scale models. Our algorithm is built upon a non uniform partitioning feature point-octree data structure. We adopt covariance analysis method to estimate the surface variation, and feature parameter in local surfaces to describe the feature. The bigger the feature parameter is, the greater likelihood the point becomes a feature point. During the process of building point-octree, feature parameter is related to each LOD node. When displaying, we combin this feature point-octree with an improved particle swarm optimizer (PSO) algorithm to implement multilevel feature fast displaying according to viewpoint and feature level.
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115-119
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Online since:
March 2012
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© 2012 Trans Tech Publications Ltd. All Rights Reserved
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