1. Landmark-based geometric morphometrics has emerged as an essential discipline for the quantitative analysis of size and shape in ecology and evolution. With the ever-increasing density of digitized landmarks, the possible development of a fully automated method of landmark placement has attracted considerable attention. Despite the recent progress in image registration techniques, which could provide a pathway to automation, three-dimensional (3D) morphometric data are still mainly gathered by trained experts. For the most part, the large infrastructure requirements necessary to perform image-based registration, together with its system specificity and its overall speed, have prevented its wide dissemination.
2. Here, we propose and implement a general and lightweight point cloud-based approach to automatically collect high-dimensional landmark data in 3D surfaces (Automated Landmarking through Point cloud Alignment and Correspondence Analysis). Our framework possesses several advantages compared with image-based approaches. First, it presents comparable landmarking accuracy, despite relying on a single, random reference specimen and much sparser sampling of the structure's surface. Second, it can be efficiently run on consumer-grade personal computers. Finally, it is general and can be applied at the intraspecific level to any biological structure of interest, regardless of whether anatomical atlases are available.
3. Our validation procedures indicate that the method can recover intraspecific patterns of morphological variation that are largely comparable to those obtained by manual digitization, indicating that the use of an automated landmarking approach should not result in different conclusions regarding the nature of multivariate patterns of morphological variation.
4. The proposed point cloud-based approach has the potential to increase the scale and reproducibility of morphometrics research. To allow ALPACA to be used out-of-the-box by users with no prior programming experience, we implemented it as a SlicerMorph module. SlicerMorph is an extension that enables geometric morphometrics data collection and 3D specimen analysis within the open-source 3D Slicer biomedical visualization ecosystem. We expect that convenient access to this platform will make ALPACA broadly applicable within ecology and evolution.

中文翻译：

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ALPACA：一种快速准确的计算机视觉方法，用于自动标记三维生物结构

1. 基于地标的几何形态计量学已成为生态和进化中大小和形状定量分析的重要学科。随着数字化地标密度的不断增加，可能开发一种全自动地标放置方法已引起相当大的关注。尽管最近在图像配准技术方面取得了进展，可以提供自动化途径，但三维 (3D) 形态测量数据仍主要由训练有素的专家收集。在大多数情况下，执行基于图像的配准所需的大型基础设施要求，以及其系统特异性和整体速度，阻碍了其广泛传播。
2. 在这里，我们提出并实施了一种基于点云的通用且轻量级的方法来自动收集 3D 表面中的高维地标数据（通过点云对齐和对应分析进行自动地标）。与基于图像的方法相比，我们的框架具有几个优点。首先，尽管依赖于单个随机参考样本和结构表面的稀疏采样，但它提供了相当的地标精度。其次，它可以在消费级个人电脑上高效运行。最后，它是通用的，可以在种内水平应用于任何感兴趣的生物结构，无论是否有解剖图谱可用。
3. 我们的验证程序表明，该方法可以恢复种内形态变异模式，这些模式在很大程度上与手动数字化获得的模式相当，这表明使用自动地标方法不应导致关于形态变异多变量模式性质的不同结论。
4. 所提出的基于点云的方法有可能增加形态测量研究的规模和可重复性。为了让没有编程经验的用户能够开箱即用地使用 ALPACA，我们将其实现为 SlicerMorph 模块。SlicerMorph 是一个扩展，可在开源 3D Slicer 生物医学可视化生态系统中实现几何形态学数据收集和 3D 标本分析。我们期望方便地访问这个平台将使 ALPACA 在生态和进化中得到广泛应用。