Data from: Tuning Geometric Morphometrics: an R tool to reduce information loss caused by surface smoothing

The application of Geometric Morphometrics has remarkably increased since 3D imaging techniques have become widespread, such as high-resolution computerised tomography, laser scanning and photogrammetry. Acquisition, 3D rendering and simplification of virtual objects produce faceting and topological artefacts, which can be counteracted by applying decimation and smoothing algorithms. Nevertheless, smoothing algorithms can have detrimental effects. This work aims at developing a method to assess the amount of information loss or recovery after the application of 3D surface smoothing.
The method presented here is conceived to optimise the smoothing procedure for 3D surfaces used in Geometric Morphometrics. We implemented the method in a tool running in the r statistical environment.
The tool requires one surface, one landmark set and one surface semilandmark set to estimate the best smoothing settings, including algorithm type, iteration and scale factor value. Additional parameters can be tuned by the user. We describe the method in detail, reporting the tool usage, including its main settable parameters. One example is provided as a further explanation of the method.
Our method reduces the chances of losing information in Geometric Morphometrics applications and is a unique attempt of standardising a widespread, potentially damaging procedure. The tool represents an advance in the application of Geometric Morphometrics.

自高分辨率计算机断层成像、激光扫描与摄影测量等三维成像技术普及以来，几何形态测量学（Geometric Morphometrics）的应用得到显著发展。虚拟对象的采集、三维渲染与简化过程会产生面状伪影与拓扑伪影，可通过应用网格精简与平滑算法加以抑制。然而，平滑算法本身也可能带来负面影响。本研究旨在开发一种方法，用于评估三维表面平滑处理后信息的损失或恢复程度。

本文提出的方法旨在优化几何形态测量学中所用三维表面的平滑流程，我们将该方法集成至一款运行于R统计环境的工具中。

该工具需输入一组三维表面、一组标志点集与一组表面半标志点集，以估算最优平滑参数设置，包括算法类型、迭代次数与缩放因子数值。用户还可自行调整其他额外参数。本文详细阐述了该方法，并介绍了工具的使用方法，包括其主要可配置参数。此外，本文还提供了一个示例以进一步说明该方法。

本方法可降低几何形态测量学应用中的信息丢失风险，亦是首次尝试对这一应用广泛却可能带来损害的流程进行标准化。该工具的推出，推动了几何形态测量学的应用发展。