Markov Random Field Surface Reconstruction |
| | Abstract | A method for implicit surface reconstruction is proposed. The novelty in this paper is the adaption of Markov Random Field regularisation of a distance field. The Markov Random Field formulation allows us to integrate both knowledge about the type of surface we wish to reconstruct (the prior) and knowledge about data (the observation model) in an orthogonal fashion. Local models that account for both scene-specific knowledge and physical properties of the scanning device are described. Furthermore, how the optimal distance field can be computed is demonstrated using conjugate gradients, sparse Cholesky factorisation, and a multiscale iterative optimisation scheme. The method is demonstrated on a set of scanned human heads and, both in terms of accuracy and the ability to close holes, the proposed method is shown to have similar or superior performance when compared to current state-of-the-art algorithms. | | Keywords | Curve, surface, solid, and object representations, Graphics data structures and data types, Statistical | | Type | Journal paper [With referee] | | Journal | IEEE Transactions on Visualization and Computer Graphics | | Year | 2009 Month November | | Publisher | IEEE Computer Society | | ISBN / ISSN | ISSN: 1077-2626 | | BibTeX data | [bibtex] | | IMM Group(s) | Image Analysis & Computer Graphics |
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