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The efficiency and accuracy loss are the key issues for the point cloud simplification. In this paper, a feature preserving algorithm is proposed for point cloud simplification based on hierarchical clustering with the surface feature description. The surface variation is presented as the main criterion for the efficient hierarchical clustering method to simplify the mass and dense point cloud fast,...
Feature sensitive simplification and re-sampling of point set surfaces is an important and challenging issue for many computer graphics and geometric modeling applications. Based on the regular sampling of the Gaussian sphere and the surface normals mapping onto the Gaussian sphere, an adaptive re-sampling framework for point set surfaces is presented in this paper, which includes a naive sampling...
Three-dimensional (3D) surface reconstruction from the point cloud has been widely discussed since two decades ago. The existing methods are either incredibly complex in computation or the quality of the meshes is improved only after the surfaces are constructed. In this paper, we propose a considerable framework to trade off between quality and complexity for reconstructing the surfaces. We suggest...
This paper studies the problem of simplifying densely distributed point-sampled models. Many computer graphics applications call for vivid, full detail models. However, the level of detail necessary is more important than this need for fidelity in rendering system. So it is useful to obtain simple versions of complex models. We have developed a novel simplification algorithm which can preserve the...
Simplification of scattered point cloud is one of the key preprocessing technologies in reverse engineering. Most simplification algorithms always lose geometric feature excessively in the process. On the basis of feature extraction, a new algorithm is proposed for the simplification of scattered point cloud with unit normal vectors. First, points in point cloud are distributed into uniform cubes...
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