Title: Continuous Global Optimization in Surface Reconstruction from an Oriented Point Cloud
Authors: Pan, Rongjiang
Skala, Václav
Citation: Computer Aided Design. 2011, vol. 43, no. 8, p. 896-901.
Issue Date: 2011
Publisher: Elsevier
Document type: preprint
URI: http://dx.doi.org/10.1016/j.cad.2011.03.005
Keywords: počítačová grafika;rekonstrukce povrchu;interpolace
Keywords in different language: computer graphics;surface reconstruction;interpolation
Abstract: We introduce a continuous global optimization method to the field of surface reconstruction from discrete noisy cloud of points with weak information on orientation. The proposed method uses an energy functional combining flux-based data-fit measures and a regularization term. A continuous convex relaxation scheme assures the global minima of the geometric surface functional. The reconstructed surface is implicitly represented by the binary segmentation of vertices of a 3D uniform grid and a triangulated surface can be obtained by extracting an appropriate isosurface. Unlike the discrete graph-cut solution, the continuous global optimization entails advantages like memory requirements, reduction of metrication errors for geometric quantities, allowing globally optimal surface reconstruction at higher grid resolutions. We demonstrate the performance of the proposed method on several oriented point clouds captured by laser scanners. Experimental results confirm that our approach is robust to noise, large holes and non-uniform sampling density under the condition of very coarse orientation information.
Rights: Original article published under copyright licence: © 2011, Elsevier. Citation of an original article: Rongjiang, Pan a Skala, Václav. Continuous global optimization in surface reconstruction from an oriented point cloud. Computer-Aided Design. 2011, 43(8), s. 896-901. doi: http://dx.doi.org/10.1016/j.cad.2011.03.005. ISSN 0010-4485.
Appears in Collections:Preprinty / Preprints (KIV)

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