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In this paper we present a fast and efficient method for generating a patient-specific femur mesh by morphing a template femur mesh of ten-node tetrahedral elements on a geometry represented in STL format (STL: Surface Tessellation Language). The morphing is constrained through a set of user defined anatomical landmark points. Our method splits the input geometries into open surfaces, and maps each surface individually on a planar parameterization. We morph the parameterizations individually using a regression model based on Radial Basis Functions (RBFs). In experiments our method shows precise results in generating new patient-specific meshes from existing models and in re-meshing.

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Historically it has been a challenge to rapidly produce a geomodel that can honor the detailed form of complex faulting and folding, while enabling sensible property modeling and that is tailored to fluid flow simulations. In structurally complex areas, the construction of accurate 3D geological models is often impeded by the complexity of the fault framework, the resulting layer segmentation, "multi-z" horizons in compressive settings and steeply dipping to overturned layers. In particular, standard geocellular models, such as pillar grids, may fail to honor complex structural features. To address those issues, methodologies using a mapping between the geological space and a 3D parametric space — often referred to as depositional space — have been described in the literature for geological grid construction and property population. Using case examples of structurally complex settings, we illustrate a depositional unstructured grid construction workflow. Compared to known methodologies, the depositional space is computed using a geomechanically-based approach. We illustrate that the methodology allows for complex structural configurations to be effectively modeled and transformed into a geocellular model honoring the full structural complexity. Our depositional unstructured model can then be populated with properties and used directly for flow simulations.