Robust surface reconstruction via dictionary learning
Robust surface reconstruction via dictionary learning
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DOI：
10.1145/2661229.2661263
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发表时间：
2014-11
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影响因子：
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通讯作者：
Shiyao Xiong;Juyong Zhang;Jianmin Zheng;Jianfei Cai;Ligang Liu
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文献类型：
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作者：
Shiyao Xiong;Juyong Zhang;Jianmin Zheng;Jianfei Cai;Ligang Liu
点云曲面重建在计算机图形学中具有重要的实际意义。现有的方法往往是通过几个阶段来实现重建，每个阶段都有各自的目标，这些阶段的整合可能不会得到最优解。在本文中，为了避免现有技术中多阶段处理的固有局限性，我们提出了一个统一的框架，将几何和连接构建作为一个联合优化问题。该框架基于字典学习，字典由重构三角网格的顶点组成，稀疏编码矩阵对网格的连通性进行编码。字典学习被表述为约束的l2，q优化（0 < q < 1），目的是找到顶点位置和三角化，最小化由点到网格度量和正则化组成的能量函数。我们的公式在同一框架内考虑了许多因素，包括距离度量，噪声/离群值弹性，尖锐特征保存，无需估计正态等，从而提供了一种全局和鲁棒的算法，能够有效地从具有缺陷的密集数据点中恢复分段光滑表面。使用合成模型、真实世界模型和公开可用基准的大量实验表明，我们的方法在准确性、对噪声和异常值的鲁棒性、几何特征和细节保存以及网格连通性方面优于最先进的方法。
Surface reconstruction from point cloud is of great practical importance in computer graphics. Existing methods often realize reconstruction via a few phases with respective goals, whose integration may not give an optimal solution. In this paper, to avoid the inherent limitations of multi-phase processing in the prior art, we propose a unified framework that treats geometry and connectivity construction as one joint optimization problem. The framework is based on dictionary learning in which the dictionary consists of the vertices of the reconstructed triangular mesh and the sparse coding matrix encodes the connectivity of the mesh. The dictionary learning is formulated as a constrained ℓ2,q-optimization (0 < q < 1), aiming to find the vertex position and triangulation that minimize an energy function composed of point-to-mesh metric and regularization. Our formulation takes many factors into account within the same framework, including distance metric, noise/outlier resilience, sharp feature preservation, no need to estimate normal, etc., thus providing a global and robust algorithm that is able to efficiently recover a piecewise smooth surface from dense data points with imperfections. Extensive experiments using synthetic models, real world models, and publicly available benchmark show that our method outperforms the state-of-the-art in terms of accuracy, robustness to noise and outliers, geometric feature and detail preservation, and mesh connectivity.