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We introduce a novel stratified sampling technique for mesh surfaces that gives the user control over sampling density and anisotropy via a tensor field. Our approach is based on sampling space-filling curves mapped onto mesh segments via parametrizations aligned with the tensor field. After a short preprocessing step, samples can be generated in real time. Along with visual examples, we provide rigorous...
We present a novel method to integrate multiple 3D scans captured from different viewpoints. Saliency information is used to guide the integration process. The multi-scale saliency of a point is specifically designed to reflect its sensitivity to registration errors. Then scans are partitioned into salient and non-salient regions through an Markov Random Field (MRF) framework where neighbourhood consistency...
We propose a novel method based on higher order Conditional Random Field (CRF) for reconstructing surface models from multi-view data sets. This method is automatic and robust to inevitable scanning noise and registration errors involved in the stages of data acquisition and registration. By incorporating the information within the input data sets into the energy function more sufficiently than existing...
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