We present an out-of-core variational approach for surface reconstruction
from a set of aligned depth maps. Input depth maps are supposed to be
reconstructed from regular photos or/and can be a representation of terrestrial
LIDAR point clouds. Our approach is based on surface reconstruction via total
generalized variation minimization ($TGV$) because of its strong
visibility-based noise-filtering properties and GPU-friendliness. Our main
contribution is an out-of-core OpenCL-accelerated adaptation of this numerical
algorithm which can handle arbitrarily large real-world scenes with scale
diversity.

利用图形处理器对超大规模深度图像进行曲面重建，其基于可见性噪音过滤特性和 GPU 友好的总体广义变分最小化（$TGV$）算法的离线部分的适应性。

利用全局 $TGV$ 最小化算法实现离线表面重建

Out-of-Core Surface Reconstruction via Global $TGV$ Minimization

Recently popularized randomized methods for principal component analysis
(PCA) efficiently and reliably produce nearly optimal accuracy --- even on
parallel processors --- unlike the classical (deterministic) alternatives. We
adapt one of these randomized methods for use with data sets that are too large
to be stored in random-access memory (RAM). (The traditional terminology is
that our procedure works efficiently "out-of-core.") We illustrate the
performance of the algorithm via several numerical examples. For example, we
report on the PCA of a data set stored on disk that is so large that less than
a hundredth of it can fit in our computer's RAM.

本文介绍了一种基于随机方法的主要成分分析算法，该算法适用于无法存储在内存中的大型数据集，并通过多个数值实例展示了其性能。