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State of the Art in Surface Reconstruction from Point Clouds
Author(s) -
Matthew Berger,
Andrea Tagliasacchi,
Lee M. Seversky,
Pierre Alliez,
Joshua A. Levine,
Andrei Sharf,
Claudio Silva
Publication year - 2014
Publication title -
hal (le centre pour la communication scientifique directe)
Language(s) - English
Resource type - Conference proceedings
DOI - 10.2312/egst.20141040
Subject(s) - point cloud , surface reconstruction , state (computer science) , computer science , surface (topology) , point (geometry) , computer graphics (images) , computer vision , geometry , algorithm , mathematics
International audienceThe area of surface reconstruction has seen substantial progress in the past two decades. The traditional problem addressed by surface reconstruction is to recover the digital representation of a physical shape that has been scanned, where the scanned data contains a wide variety of defects. While much of the earlier work has been focused on reconstructing a piece-wise smooth representation of the original shape, recent work has taken on more specialized priors to address significantly challenging data imperfections, where the reconstruction can take on different representations -- not necessarily the explicit geometry. This state-of-the-art report surveys the field of surface reconstruction, providing a categorization with respect to priors, data imperfections, and reconstruction output. By considering a holistic view of surface reconstruction, this report provides a detailed characterization of the field, highlights similarities between diverse reconstruction techniques, and provides directions for future work in surface reconstruction

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