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Multi-View Oriented 3D Data Processing

Abstract : Point cloud refinement and surface reconstruction are two fundamental problems in geometry processing. Most of the existing methods have been targeted at range sensor data and turned out be ill-adapted to multi-view data. In this thesis, two novel methods are proposed respectively for the two problems with special attention to multi-view data. The first method smooths point clouds originating from multi-view reconstruction without impairing the data. The problem is formulated as a nonlinear constrained optimization and addressed as a series of unconstrained optimization problems by means of a barrier method. The second method triangulates point clouds into meshes using an advancing front strategy directed by a sphere packing criterion. The method is algorithmically simple and can produce high-quality meshes efficiently. The experiments on synthetic and real-world data have been conducted as well, which demonstrates the robustness and the efficiency of the methods. The developed methods are suitable for applications which require accurate and consistent position information such photogrammetry and tracking in computer vision
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Submitted on : Friday, March 30, 2018 - 9:53:29 AM
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  • HAL Id : tel-01754569, version 1



Kun Liu. Multi-View Oriented 3D Data Processing. Other [cs.OH]. Université de Lorraine, 2015. English. ⟨NNT : 2015LORR0273⟩. ⟨tel-01754569⟩



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