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Signing the Unsigned: Robust Surface Reconstruction from Raw Pointsets
Abstract : We propose a modular framework for robust 3D reconstruction from unorganized, unoriented, noisy, and outlierridden geometric data. We gain robustness and scalability over previous methods through an unsigned distance approximation to the input data followed by a global stochastic signing of the function. An isosurface reconstruction is finally deduced via a sparse linear solve. We show with experiments on large, raw, geometric datasets that this approach is scalable while robust to noise, outliers, and holes. The modularity of our approach facilitates customization of the pipeline components to exploit specific idiosyncracies of datasets, while the simplicity of each component leads to a straightforward implementation.
Cited literature [17 references]
https://hal.inria.fr/inria-00502473
Contributor : Pierre Alliez Connect in order to contact the contributor
Submitted on : Thursday, July 15, 2010 - 9:39:20 AM
Last modification on : Saturday, January 27, 2018 - 1:31:40 AM
Long-term archiving on: : Thursday, December 1, 2016 - 8:28:37 AM
Contributor : Pierre Alliez Connect in order to contact the contributor
Submitted on : Thursday, July 15, 2010 - 9:39:20 AM
Last modification on : Saturday, January 27, 2018 - 1:31:40 AM
Long-term archiving on: : Thursday, December 1, 2016 - 8:28:37 AM
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signing.pdf
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