Motion tracking in narrow spaces: a structured light approach
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Real time surface registration for PET motion tracking
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
A Global Parity Measure for Incomplete Point Cloud Data
Computer Graphics Forum
Statistical surface recovery: a study on ear canals
MeshMed'12 Proceedings of the 2012 international conference on Mesh Processing in Medical Image Analysis
Mesh saliency via spectral processing
ACM Transactions on Graphics (TOG)
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A method for implicit surface reconstruction is proposed. The novelty in this paper is the adaption of Markov Random Field regularization 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 factorization, and a multiscale iterative optimization 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.