Reliable Surface Extraction from Point-Clouds using Scanner-Dependent Parameters
Reliable Surface Extraction from Point-Clouds using Scanner-Dependent Parameters
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DOI：
10.3722/cadaps.2013.265-277
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发表时间：
2013
影响因子：
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通讯作者：
H. Masuda;Ichiro Tanaka;M. Enomoto
中科院分区：
文献类型：
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作者：
H. Masuda;Ichiro Tanaka;M. Enomoto
摘要基于相位和飞行时间的激光扫描仪可用于捕获工业厂房的密集点云。我们的目标是重建工业工厂组件的 3D 模型。对于稳健地提取曲面，曲面拟合中残差的标准差对结果有很大影响。标准差是最小二乘法、稳健估计、区域生长和RANSAC中的基本参数之一。然而，估计大范围内拟合误差的标准差并不容易，因为点云中的标准差根据扫描物体的尺寸、距离和材料而变化。在本文中，我们使用实验数据研究曲面拟合中残差的分布，并推导测量误差标准差的预测函数。我们的实验结果表明，我们的预测函数对于可靠地提取不同尺寸、距离和材料的表面是有效的。
AbstractPhase-based and time-of-flight laser scanners can be used to capture dense point- clouds of industrial plants. Our goal is to reconstruct 3D models of components in industrial plants. For robustly extracting surfaces, the standard deviation of residuals in surface fitting has large impact on the result. The standard deviation is one of basic parameters in the least-squares, robust estimate, region growing, and RANSAC. However, it is not easy to estimate the standard deviations of fitting errors in a wide range of field, because the standard deviations vary in a point-cloud, according to the sizes, distances, and materials of scanned objects. In this paper, we investigate the distributions of residuals in surface fitting using experimental data, and derive prediction functions of the standard deviations for measurement errors. Our experimental result shows that our prediction functions are effective for reliably extracting surfaces of diverse sizes, distances, and materials.