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A Survey of Surface Reconstruction from Point Clouds
Author(s) -
Berger Matthew,
Tagliasacchi Andrea,
Seversky Lee M.,
Alliez Pierre,
Guennebaud Gaël,
Levine Joshua A.,
Sharf Andrei,
Silva Claudio T.
Publication year - 2017
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12802
Subject(s) - surface reconstruction , point cloud , 3d reconstruction , surface (topology) , representation (politics) , computer science , field (mathematics) , prior probability , iterative reconstruction , artificial intelligence , categorization , computer vision , point distribution model , mathematics , geometry , bayesian probability , politics , political science , pure mathematics , law
The 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 contain 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. We survey the field of surface reconstruction, and provide a categorization with respect to priors, data imperfections and reconstruction output. By considering a holistic view of surface reconstruction, we show a detailed characterization of the field, highlight similarities between diverse reconstruction techniques and provide directions for future work in surface reconstruction.