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How much Geometry it takes to Reconstruct a 2-Manifold in R^3

MPS-Authors
/persons/resource/persons44353

Dumitriu,  Daniel
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

/persons/resource/persons44464

Funke,  Stefan
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

/persons/resource/persons44874

Kutz,  Martin
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

/persons/resource/persons45051

Milosavljevic,  Nikola
Algorithms and Complexity, MPI for Informatics, Max Planck Society;

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Citation

Dumitriu, D., Funke, S., Kutz, M., & Milosavljevic, N. (2008). How much Geometry it takes to Reconstruct a 2-Manifold in R^3. In Proceedings of the 10th Workshop on Algorithm Engineering and Experiments (pp. 65-74). Philadelphia, PA: SIAM.


Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-1BDF-1
Abstract
Known algorithms for reconstructing a 2-manifold from a point sample in R3 are naturally based on deci- sions/predicates that take the geometry of the point sample into account. Facing the always present problem of round-off errors that easily compromise the exactness of those predi- cate decisions, an exact and robust implementation of these algorithms is far from being trivial and typically requires the employment of advanced datatypes for exact arithmetic as provided by libraries like CORE, LEDA or GMP. In this paper we present a new reconstruction algorithm, one of whose main novelties is to throw away geometry informa- tion early on in the reconstruction process and to mainly operate combinatorially on a graph structure. As such it is less susceptible to robustness problems due to round-off errors and also benefits from not requiring expensive exact arithmetic by faster running times. A more theoretical view on our algorithm including correctness proofs under suitable sampling conditions can be found in a companion paper [3].