IEEE Trans Image Process - An orientation inference framework for surface reconstruction from unorganized point clouds.

Tópicos

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Resumo

In this paper, we present an orientation inference framework for reconstructing implicit surfaces from unoriented point clouds. The proposed method starts from building a surface approximation hierarchy comprising of a set of unoriented local surfaces, which are represented as a weighted combination of radial basis functions. We formulate the determination of the globally consistent orientation as a graph optimization problem by treating the local implicit patches as nodes. An energy function is defined to penalize inconsistent orientation changes by checking the sign consistency between neighboring local surfaces. An optimal labeling of the graph nodes indicating the orientation of each local surface can, thus, be obtained by minimizing the total energy defined on the graph. The local inference results are propagated over the model in a front-propagation fashion to obtain the global solution. The reconstructed surfaces are consolidated by a simple and effective inspection procedure to locate the erroneously fitted local surfaces. A progressive reconstruction algorithm that iteratively includes more oriented points to improve the fitting accuracy and efficiently updates the RBF coefficients is proposed. We demonstrate the performance of the proposed method by showing the surface reconstruction results on some real-world 3-D data sets with comparison to those by using the previous methods.

Resumo Limpo

paper present orient infer framework reconstruct implicit surfac unori point cloud propos method start build surfac approxim hierarchi compris set unori local surfac repres weight combin radial basi function formul determin global consist orient graph optim problem treat local implicit patch node energi function defin penal inconsist orient chang check sign consist neighbor local surfac optim label graph node indic orient local surfac can thus obtain minim total energi defin graph local infer result propag model frontpropag fashion obtain global solut reconstruct surfac consolid simpl effect inspect procedur locat erron fit local surfac progress reconstruct algorithm iter includ orient point improv fit accuraci effici updat rbf coeffici propos demonstr perform propos method show surfac reconstruct result realworld d data set comparison use previous method

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