Title of Paper:Continuous global optimization in surface reconstruction from an oriented point cloud

Journal:Computer-Aided Design

Key Words:Surface reconstructionContinuous global optimizationConvex relaxation

Summary: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

Indexed by:Journal paper

Discipline:Engineering

First-Level Discipline:Software Engineering

Document Type:J

Volume:43

Issue:8

Number of Words:6000

Translation or Not:No

Date of Publication:2011-08

Included Journals:SCI

Links to Published Journals:https://www.sciencedirect.com/science/article/pii/S0010448511000662