Improving point cloud to surface reconstruction with generalized Tikhonov regularization
Improving point cloud to surface reconstruction with generalized Tikhonov regularization
复制标题
利用广义吉洪诺夫正则化改进点云到表面的重建
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
F. Pereira
中科院分区：
文献类型：
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作者：
André F. R. Guarda;J. Bioucas;Nuno M. M. Rodrigues;F. Pereira
对于采用基于点云表示的应用程序，点云渲染在用户体验质量中起着至关重要的作用。虽然这不是一个新的领域，但它最近变得与主要标准化组织（特别是JPEG和MPEG）最近对点云编码的兴趣更加相关。筛选泊松曲面重建是一种最先进的技术，用于从点云样本生成防水表面网格。虽然其筛选组件允许表面更好地拟合云点，但这种拟合可能导致表面中出现不期望的伪影，特别是当点云有噪声时。本文提出了一种改进的重建方法，通过采用广义Tikhonov正则化项使其对噪声更具鲁棒性。所提出的正则化方法平滑了应该平坦的区域，同时保留了边缘中的重要细节，从而创建了更令人愉快的表面重建。
Point cloud rendering has a vital role in the user Quality of Experience for applications adopting point cloud based representations. While this is not a new area, it has recently become more relevant with the recent interest on point cloud coding by major standardization groups, notably JPEG and MPEG. The screened Poisson surface reconstruction is a state-of-the-art technique for generating a watertight surface mesh from the point cloud samples. While its screening component allows the surface to better fit the cloud points, this fitting may lead to undesired artifacts in the surface, notably when the point cloud is noisy. This paper proposes to improve this reconstruction method by making it more robust to noise by adopting a generalized Tikhonov regularization term. The proposed regularization approach smooths regions that should be flat while keeping the important details in the edges, thus creating more pleasant surface reconstructions.