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Out-of-Core Surface Reconstruction via Global $TGV$ Minimization
arXiv - CS - Computer Vision and Pattern Recognition Pub Date : 2021-07-30 , DOI: arxiv-2107.14790
Nikolai Poliarnyi

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.

中文翻译:

通过全局 $TGV$ 最小化进行核外表面重建

我们提出了一种核外变分方法,用于从一组对齐的深度图重建表面。输入深度图应该从常规照片或/并且可以是陆地 LIDAR 点云的表示重建。由于其强大的基于可见性的噪声过滤特性和 GPU 友好性,我们的方法基于通过总广义变化最小化 ($TGV$) 进行的表面重建。我们的主要贡献是对这种数值算法的核心外 OpenCL 加速适应,该算法可以处理具有尺度多样性的任意大的现实世界场景。
更新日期:2021-08-02
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