Fan-Meshes:用于基于点的3D模型和场景描述的几何原语

Xiaotian Yan, Fang Meng, H. Zha
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引用次数: 1

摘要

我们提出了一种名为Fan-Meshes (FM)的数据结构,用于重建由密集扫描点云表示的三维模型和场景。它是数据几何的局部分段线性逼近,可以作为重构的原语,在计算负荷和重构质量之间取得很好的平衡。在该算法中,在预处理过程中进行局部重划分以获得规则的FMs,然后使用一种称为三角选择记录(TSR)的三水平点数据结构来减少原始数据中的冗余和原始FMs中的重叠。此外,为了将该方法应用于原始3D扫描数据,我们对点云使用平滑算子来消除一些传感器噪声。实验结果表明,该方案在具有真实数据的大规模场景下也是有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fan-Meshes: a geometric primitive for point-based description of 3D models and scenes
We propose a data structure, called Fan-Meshes (FM), for reconstructing 3D models and scenes represented by dense scanning point clouds. It is a local piecewise linear approximation to the data geometry, and can serve as primitives in reconstruction with a good balance between computational loads and reconstruction quality. In our algorithm, local remeshing is performed in preprocessing to obtain regular FMs, and a three-level-point data structure called triangle selection record (TSR) is then used to reduce redundancies in the raw data and overlapping in the original FMs. Furthermore, to apply the method to raw 3D scanning data, we use a smoothing operator to the point cloud in order to eliminate some sensor noises. Experimental results demonstrate that our scheme is effective even for large-scale scenes with real data.
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