曲面重建
点云
三维重建
曲面(拓扑)
代表(政治)
计算机科学
领域(数学)
先验概率
迭代重建
人工智能
分类
计算机视觉
点分布模型
数学
几何学
贝叶斯概率
政治
政治学
法学
纯数学
作者
Matthew Berger,Andrea Tagliasacchi,Lee M. Seversky,Pierre Alliez,Xavier Granier,Joshua A. Levine,Andrei Sharf,Claudio Silva
摘要
Abstract 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.
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