The central goal of the project is to research and develop high-performance variational methods for large scale 3D reconstruction problems, which are general and accurate while meeting computation time constraints imposed by visual computing applications. Key abilities will be that
What is the best geometric representation to implement locally adaptive geometry optimization?
Which variational models efficiently allow local refinement of the scene structure in a mathematically consistent manner?
Which models in shape reconstruction best translate to the adaptive shape representation?
How can the local refinement of surface properties (e.g., texture) be modeled and optimized—ideally together with the geometry?
What are natural, ideally convex priors on adaptive grids?
What are optimal local accuracy measures for different applications?
How can an ideal trade-off between accuracy and run-time be determined?
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